自动语法改错(GEC),是自然语言处理中句法分析中较为困难的任务之一。在日常对话中，语法上的细微差别对于一个非母语的人来说是最困难掌握与理解的，当前自然语言中的语法改错不仅包含语法错误，也包含拼写与搭配错误。
    近年来，随着深度学习的发展，自动语法改错任务得到了不少关注。基于统计机器翻译(SMT)的短语相关方法，是将GEC 看做一个翻译任务：从“坏”转换到“好”，所用的语料也是类似翻译语料的平行语料。不同于SMT 依赖于递归神经网络（RNN），也有通过卷积神经网络（CNN）来进行句子编码，提取以短语为基础的语义空间表征。这些方法都是通过建立端到端（encoder-decoder）的序列到序列（seq2seq）模型,理解
错误句子与正确句子之间的语义以及词语表述的差异来定位语法错误。
    为了进一步充分学习数据中的知识，通过监督学习（supervised learning）方式是最常见的。该方法需要大量标注数据，但是标注成本巨大。学者们发现可以利用非标注（unlabeled）数据进行非监督学习,通过挖掘其中有价值语义信息帮助其他的监督任务理解。其中有利用基于翻译语料的预训练模型,也有利用长文本语料进行语言模型的预训练,还有利用多任务结合的泛化性预训练模型。这些预训练模型都在许多任务
上经过检验，可以对模型表现有很大的提升。
    虽然自动改错模型可以借助比较新颖的模型架构，但是由于自动改错语料的缺失，更大范围的自动改错以及具有实际应用价值的自动改错模型建设依然不理想。而本次研究不仅提出了一种新的堆叠模型结构，同时该结构可将预训练的丰富语义信息的特征嵌入，得到一种可适配多种预训练方法的多层自动纠错模型。模型不仅可以进行多轮迭代解决改错难题，同时为了进一步缓解自动改错语料不足，利用了对偶学习方法产生更多额外训练数据。整体纠错框架不仅可以帮助理解词语之间的相关性、短语的
连贯性、语义的匹配性，还有句子语法准确性。
    阶段式的模型结构，使得模块能高度可替换且可扩充。同时目前已经开源平行纠错语料以及实际改错样例表明，该模型不仅可以在学术数据集取得很不错的效果还能应用到实际场景。本文模型框架还能进一步融合目前最新的预训练模型权值，具有很强的可扩展性，这是其他所有工作所不具备的。使得本次研究更有意义以及未来研究价值。

   Automatic grammar correction (GEC) is one of the most difficult tasks in syntactic analysis in natural language processing. In daily conversations, grammatical nuances are the most difficult to grasp and understand for a non-native speaker. The grammatical corrections in current natural language include not only grammatical errors, but also spelling and collocation errors.
    In recent years, with the development of deep learning, the task of automatic grammar correction has received a lot of attention. The phrase-related method based on statistical machine translation (SMT) is to regard GEC as a translation task: from "bad" to "good", the corpus used is parallel corpus similar to translation corpus. Different from SMT, which relies on recurrent neural network (RNN), there are also convolutional neural networks (CNN) for sentence coding and extraction of phrase-based semantic spatial representation. These methods locate grammatical errors by establishing an encoder-decoder sequence-to-sequence (seq2seq) model that understands the semantics between erroneous sentences and correct sentences and the differences in word expressions.
    In order to further fully learn the knowledge in the data, supervised learning is the most common. This method requires a lot of annotation data, but the cost of labeling is huge. Scholars have found that unsupervised learning can be performed using unlabeled data to help other supervisory tasks understand by mining valuable semantic information. Among them are pre-training models based on translation corpus, pre-training using long text corpus, and generalized pre-training model using multi-task. These pre-training models have been tested on many tasks and can greatly improve the performance of the model.
    Although the automatic error correction model can be based on a relatively new model architecture, the automatic error correction and the automatic error correction model with practical application value are still not ideal due to the lack of automatic error correction corpus. This study not only proposes a new stacked model structure, but also embeds the features of pre-trained rich semantic information to obtain a multi-layer automatic error correction model that can adapt to multiple pre-training methods. The model can not only solve the problem of correcting errors by multiple rounds of iteration, but also use the dual learning method to generate more additional training data in order to further alleviate the lack of automatic error correction corpus. The overall error correction framework can not
only help to understand the correlation between words, the coherence of phrases, the matching of semantics, but also the accuracy of sentence grammar.
    The staged model structure makes the module highly replaceable and expandable. At the same time, the open source parallel error correction corpus and the actual error correction
examples show that the model can not only achieve good results in the academic data set but also apply to the actual scene. The model framework of this paper can further integrate the current pre-training model weights, which is highly scalable, which is not available in all other work. Make this study more meaningful and future research value.

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已有二语习得研究表明，提供有效校正反馈，有利于提高第二语言学习者语言水平。目前市面上也出现了一些英语写作的纠错工具，例如国外有LanguageTool、Grammarly，国内有批改网等软件。这些工具大多局限于英文写作中的单词拼写错误、语法错误，当涉及中式英语、搭配错误、句型错误、含义模糊等偏主观错误时，主要依靠人工制定规则进行识别。另外，虽然已有LanguageTool等开源工具可以进行错误识别，但是不能针对规则特点灵活进行适配和更改，且识别速度较慢。针对以上问题，本研究提出利用已标注的学习者语料，从中半自动地抽取检错规则，然后自行开发轻量级的规则匹配器来验证和应用规则。

在本研究中，首先提取了英文改错规则。通过对已标注的学习者语料库CLEC和NUCLE进行详尽分析，确定可由程序自动抽取的错误类别；通过Java程序设计算法实现规则的初步提取，并且将抽取结果写入MySQL数据库。并且对抽取之后的规则进行测试和验证，通过人工方式筛选规则。最后合理利用牛津搭配词典、Google Books等语料资源对现有规则进行延伸，以达到通过订正错误来帮助学习者学习英语的目的。

其次，笔者设计和实现了轻量级规则匹配器。本匹配器是针对抽取出的规则进行设计，可以对半自动抽取的规则表进行验证，也可证明从学习者语料库中半自动抽取规则的可行性。

本研究的成果是通过科学方法从学习者语料库中抽取英文改错规则，识别准确率达90%以上；并且对规则进行了预处理，为后续专家校正提供了可靠依据，减少了时间成本；另外设计并实现了轻量级的规则匹配器，作为LanguageTool的补充，将速度提升30%以上，可以高效处理各种自定义规则。研究表明，此半自动生成规则与应用的方式，提高了效率，节省了人力，能够给英语学习者以帮助。同时此项目具有通用性和易扩展性，对于其他学习者语料库或语料资源，可以很好地进行扩展和进一步研究。

The studies on the learn of the second language has shown that providing effective correction feedback is beneficial for the learners to develop the ability of learning the second language. At present, there are some error correction tools for English writing on the market, such as the foreign LanguageTools and Grammarly, and the domestic Pigai. org, etc. However, most of these tools are limited to the word spelling errors and grammatical errors in English writing, while the retrieval is mainly relied on manual rules when the rules involve subjective errors, such as chinglish, mismatches, sentence pattern errors and ambiguous meanings and so on. Although the open source tools, such as LanguageTool, etc., can identify the errors, they are not appropriate to adapt and change the rules flexibly. Besides, the recognition speed is slow, too. Regarding the issue above, this study aims to use the annotated learner corpus to extract the the error detection rules semi-automatically, and then develop a lightweight rule matcher to verify and apply the extracted rules. 

The first part is the extraction of English correction rules. Firstly, the error categories that can be automatically extracted by the program are determined through the detailed systematic analysis of the existing and annotated learner corpus CLEC and NUCLE. Secondly, the initial extraction of the rules is implemented by the design algorithm of Java program and the extraction results are written into the MySQL database. In addition, the rules after extraction are tested and verified, and then filtered manually. Finally, the resources, such as Oxford collocation dictionary and Google books, etc., are used to extend the existing rules, so as to help the learners learn English through correcting errors. 

The second part is the design and implementation of lightweight rule matcher. This matcher is developed to design the extracted rules and the existing rule base. On the one hand, the semi-automatically extracted rule table can be verified conveniently. On the other hand, the feasibility of the rules that are semi-automatically extracted from the learner corpus can be proved. 

The result of this research is that English error correction rules can be extracted from the learner corpus through scientific methods, with an accuracy rate of over 90%. Moreover, the rules are preprocessed, which provides a reliable basis for the subsequent experts to perform correction and reduce the time cost. Furthermore, the lightweight rule matcher is designed and implemented, which can be taken as a complement to LanguageTool, making the speed increase more than 30% to efficiently handle various customized rules. The studies have shown that the rules generated semi-automatically and the mode of application can improve efficiency, save manpower and help English learners. At the same time, this project has universality and extensibility, so it can extend and further research the future learner corpora or other resources. 

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近年来互联技术逐渐变得成熟，尤其是智能手机和一些数码设备的普及，令网络 上覆盖着大量的视频信息，面对急剧增长的视频数量，一些含有暴力和色情的视频内 容被肆意传播，这给青少年的身心健康带来了一定的危害，并且也给网络的监管带来 了巨大的压力。由于监控视频数量不断地增长，互联网上视频数量的持续增长令人们 对视频内容的理解以及视频中人体行为分析的需求也在不断地增加。使用计算机不仅 能够更好地理解视频中的内容，而且能够避免人们花费大量的时间对视频进行分析。

深度学习在计算机视觉领域做出了很大的贡献。将深度神经网络在大规模数据集 上进行训练使得深度学习方法在目标检测，图像分类和视频中的人体动作识别等领域 都达到了较好的效果。由于深度学习对图像数据具有很好地抽象建模能力以及能够自 动提取图像特征，而视频可看成是一系列的图像帧堆叠而成。所以对于本文研究的对 视频中的人物行为进行识别的技术采用深度学习方法来进行探索。

本文的主要工作内容如下:

现有的基于双流卷积网络的行为识别方法中用的卷机网络大部分是 BN-Inception 结构或者是 VGG 结构，这样的结构参数量较大不易于网络的训练，因此本文采用 Densenet 结构来分别提取视频的空间信息和时间信息，原有的 Densenet 结构采用的是 全局的密集连接方式，即网络中的 Dense 块中的某一层都与其它层互相连接，这样容 易造成特征冗余且参数量较大，并且由于每一层的输入都是之前所有层输出的特征映 射的拼接，所以在网络的前向传播和反向传播的过程中都要存储这些中间层的特征映 射，所以原有的 Densenet 在训练过程中占有的内存较大。本文针对上述问题，对原有 的 Densenet 做出改进参首先将原有的 Densenet 中的每一层互相连接改成局部连接，也 就是每一层只与之前的一些层部分连接，这大大减少了模型在学习过程中需要训练的 参数量。并且采用共享内存的方式减少模型占有的内存。其次，现有的双流卷机网络 最后再对人体行为进行预测时是将两个网络的结果加权平均，这样没有更好的利用视 频的时空信息，所以本文通过将提取到的视频信息在空间维度和时间维度上进行合并。

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英语写作是国内学生英语能力的短板。目前，国内学校开设的英语写作公共课程的教学效果相对有限，市面上虽然有针对英语写作的学习网站和书籍，但大多都是模板句型等资源的汇集展示，向学习者即时提供的指导不具备针对性。各类英语写作辅助工具主要提供作文的机器评阅和自动打分，仅能帮助用户发现作文中的常见错误，对于正确的内容无法予以改进指导。

对写作学习者来说，参考已有的专业或优秀的行文表达是写作学习的有效途径，语料库作为真实的语言资源知识库可以在写作学习及教学中提供可信赖的指导意见。但现阶段语料库的设计大部分面向学术研究，普通的教师和学生使用起来并不方便，具体表现在：(1)功能繁多、查询参数的设置较为复杂，用户学习使用的成本较高；(2)经常会出现前几条检索结果是长难句的情况，用户的阅读体验不佳；(3)查询结果中难免包含复杂的词汇和语句，对于普通学习者来说会有一定的阅读负担。

本研究将从辅助英语写作的角度出发，面向普通学生和老师设计并实现一个语料库查询系统，使用户可以便捷有效地获取语料信息、并利用语料库查询手段帮助发现和改正作文中的错误及不足。

本文首先期望解决目前语料库查询系统对于普通英语学习者的易用性问题，包括功能使用不便和语料阅读困难。本系统针对这些问题实现了语料库基础检索模块、检索结果重构模块和句子辅助阅读模块。基础检索模块提供常用的语料库查询功能，且具备简易查询模式，可以提升语料库使用的便捷性；检索结果重构模块的主要目的在于提升语料阅读体验，将对例句按照从易到难的顺序进行排序，并对查询结果中的用户陌生词汇进行特殊显示；句子辅助阅读模块旨在帮助用户习得语料，将提供句子的机器翻译结果、句法拆解结果和简单句等信息。

本文接下来研究了语料库查询在写作场景中的应用，着眼于解决国内学生在英文写作中常出现的词汇搭配僵化、搭配表达偏口语化等具体问题实现了语料库查询辅助写作/批改模块。该模块提供了作文搭配抽取、搭配丰富程度分析和搭配校验三个功能，可以利用语料库数据来帮助用户审阅与改进作文中的搭配使用。

经测试，检索结果重构有效提高了用户对语料的阅读兴趣；句子辅助阅读在长难句理解方面的辅助效果得到了被试者的一致认可；语料库辅助写作可以实际改善作文中词汇搭配错误及搭配重复使用的问题，在对10篇作文进行修改后，作文在批改网的得分平均提高了1.9分（满分100），最高提升了5分。

English writing is a major problem for Chinese students. English writing classes opened by domestic schools have relatively limited effects. Although there are websites and books aiming to help students write English articles, most of them are simply the displays of resources like sentence patterns. They do not provide users with adaptive instructions. Assisted tools for English writing mostly can not instruct learners to reach a higher level of English writing as they can not provide guidance on right sentences, all they do is examining articles, picking out common mistakes and offering grades.

Referring to idiomatic articles is a valid way for learners to improve writing ability. Corpus, being a data bank of language, can provide reliable guidance for writing learning and teaching. But the corpuses nowadays are mostly designed for academic purposes which do not fit the goals of common teachers and students. Actually, teachers and students may find such corpuses inconvenient when using them because: 1) They have redundant functions and complicated query parameter settings so that they are hard for users to learn. 2) It is often the case that the first few search results are long and difficult, which gives users a hard time in reading. 3) Complex words and sentences in the query results can be a burden for average learners.

Motivated by the idea of assisting English writing, this study designs and implements a corpus query system for ordinary students and teachers Using the system, users can easily obtain corpus information and use corpus query methods to help identify and improve deficiencies in the composition.

This article wishes to tackle the usability problems including the inconvenience in using as well as reading corpus data. To overcome these obstacles, the system is designed with basic query module, query results automatic reconstructing module and assisting sentence reading module. Basic query module was embedded with simple query mode which makes the corpus more convenient to use. Result reconstruction module aims at enhancing reading experience, it can reconstruct query results by sorting the example sentences in order of difficulty and highlight the unfamiliar words. Assisting sentence reading module provides syntactic splitting results (including clause recognition and collocation), machine translation results and information of simple sentences etc.

This study also explores the application of corpus query in English writing. Towards the issue of lexical fossilization and colloquialism of Chinese students, the author develops the corpus assisting writing/examing module to help them write articles by querying corpus. This module not only extracts collocations but also analyzes their richness and verifies them. It helps users examine and improve their use of collocation.

Test shows that the result reconstruction validly boost users’ interest in reading the corpus query result. The assisted sentence reading module was widely welcomed by the subjects when tested on understanding the long difficult sentences. The assisted writing module improves the use of collocation in articles. On average, 10 revised articles graded on Pigaiwang (a website provides grading module) are elevated by 1.9 points (full credit 100) after using the system for modification. Most points elevated is 5.

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经方是中医经典方剂的略称，靶点是药物发挥临床疗效时在人体内发生作用的结合位点。纵观国内外文献，基于经方进行靶点研究的文献并不多。究其原因，中医经方在国际上的影响力有限，国外学者受制于语言限制，以经方为单位进行靶点研究文献寥寥可数。国内学者虽然有天然的语言优势，但是受制于诊疗理念的不同，以经方为单位研究靶点的文献数量不多。而且，以经方为单位进行靶点研究，要人工检索文献，确定古籍中经方的配方，以及书籍和期刊论文中的经方成分，恰当处理同义词，将成分翻译成恰当的英文，再从靶点数据库获取对应的靶点，然后才能开始进行实验研究。部分经方配方有多个版本，每个配方有多种药材，药材又有众多成分，成分又对应着不同的靶点，且这些术语在不同文献中表达方式也不统一，可见科研人员在进行真正的医学实验之前，要进行大量的文献调研工作。所以，本文旨在从文献中发现经方的靶点，给科研人员提供一定的参考，减轻文献调研的压力。

从文献中发现经方的靶点，最简单的方式就是直接找到研究经方靶点的文献，提取信息。但是，由于这类文献较少，所以本文构建了从经方到靶点的不同路径，建立从经方到靶点之间的联系。经方由药材构成，药材又由成分组成，成分和靶点在靶点数据库中可以建立直接联系。因此，本文先提取经方名称、经方配方和经方成分，然后进行靶点提取，再借助靶点数据库，构建靶点筛选模型，最后将不同来源的靶点汇总，计算靶点置信度，给出经方靶点预测的列表。

本文将经方靶点预测的业务流程分解为经方配方提取、经方成分提取和经方靶点获取和经方靶点预测四个模块，并围绕这四个模块展开了关键技术的研究。

在配方提取上，本文实现了通过解析多本经典书籍提取经方配方，自动化对比结果，并给出相对一致的经方配方，解决了多版本文献资源提取关键信息的问题，这部分源于业务却不拘泥于业务本身，相反是通过简单的业务流程，来验证技术的可行性，为处理类似问题提供一个解决方法。

在成分提取上，本文通过期刊影响因子和是否为核心期刊对期刊质量进行分类，以经典书籍与优质期刊论文为主，一般文献作为辅助，用规则和统计结合的方式，提取成分术语。在成分提取模型中，本文先用通用分词工具切词，筛选关键句，再使用最大逆匹配对关键句重新切词分词，保证了包含特殊符号的成分术语可以被切分并提取出来。同时，本文还引入了添加了规则的Bi-gram模型，并通过计算词频、互信息和信息熵来发现来新的成分术语，减轻了切分词工具对初始分词词典的依赖。

在靶点获取上，文本提出了两种方式。第一种方式是通过正则表达式直接从文献中提取靶点，先直接提取经方的靶点，再提取其配方药材，然后提取经方成分的靶点。提取方式基本相同，但是三者与经方靶点的相关性依次递减，所以对应的靶点置信度也以相应减小。另一种是先借助靶点数据库获取经方成分的靶点，再通过对成分和靶点共现文献进行分类，来实现对靶点的筛选。本文先从经典书籍中获取成分的英文翻译，再从Drugbank靶点数据库中获取对应成分的靶点。我们认为如果成分和靶点的关系是可信的，那么二者的共现文献中就一定有合适的文献参考。也就是说，我们通过这种方式，将靶点筛选问题转化为经方成分和靶点共现文献分类问题。所以，我们获取了经方和靶点共现的文献，并对文献进行人工标注，把可以佐证靶点的文献作为靶点依据文献，将是否为靶点依据文献看成一个二分类问题，并将是否具有靶点依据文献作为靶点筛选的初步依据。我们选取了几种经典的文本特征，用朴素贝叶斯、KNN和SVM分类器分别进行特征寻优对比实验，最终确定了用信息增益和卡方检验结合作为特征，用SVM分类模型进行靶点依据文献分类，借此实现了靶点的初步筛选。

最后，本文构建了综合的经方靶点预测模型，提出了经方靶点置信度评分模型，根据靶点的来源、靶点依据文献的数量和相关度对靶点进行评分。本文还用构建的预测模型进行了综合实验，先以芍药甘草汤为例进行阈值寻优实验，再以大黄黄连泻心汤和四逆汤为例，验证预测模型的有效性。

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科技简报是科技情报类公文中一个重要的文种，能够为各级决策机关制定科技政策提供参考。但随着“大数据时代”的到来，传统的科技情报内涵、组织模式与应用发生了不同形式的变化，如何从海量的科技报告数据源中提炼出各类重要信息是当前科研人员所面临的重要挑战之一。目前已有的科技简报自动生成系统的核心技术是文本生成，对科技简报的文本特点不具针对性，从而对科技报告的资源使用率不高。另外，现有的文本生成技术不能有效地体现科技政策语料中的丰富知识网络信息。根据以上情况，本文分析并实验前人的研究成果，主要将历年来科技研究所公布的科技简报与其原科技政策文本进行对比分析，通过回溯的方法研究如何从冗长的科技政策文本生成简短的科技简报。其中涉及的关键技术包括概念关系标引，知识网络构建，网络表示学习以及文本生成等，本文最后运用这些技术实现了科技简报自动生成的功能。

本文首先对科技政策类文本的文本特征进行分析，分别依照概念与概念间关系的分类体系，对科技政策文本中的概念与概念关系进行自动标引。其中，概念标引采用基于RNN+CRF的深度学习方法，实现在句子中自动识别概念词汇并添加类别标签。关系标引主要分析概念间的关系特征，并采用基于SVM主动学习分类方法，为概念实体对自动标引关系。实验表明本文使用的方法能够有效地标引科技政策文本中的概念词并预测概念词之间的关系。

在实现科技政策文本自动标引后，本文进一步研究了如何通过概念与概念关系构建知识网络，并分别提出了概念知识网络和融合篇章结构的知识网络构建方法。基于此知识网络模型，本文采用了一种能够融合节点语义、拓扑结构以及类别标签信息的网络表示学习模型，并引入Node2vec算法和知识推理信息对该模型进行改进。同时，本文还对带篇章结构的知识网络中的篇章节点表示进行分析。最后通过SVM分类器和可视化方式，证明本文提出的网络表示学习方法能更加有效地表示知识网络中的节点。

最后，本文将知识网络节点表示分别应用在基于单篇的和基于多篇的科技简报篇章结构和内容的自动生成中。对于科技简报篇章结构的生成，本文采用保留原文结构或者选取重要篇章节点的方法。对于文本内容生成，本文研究了抽取式和生成式两种方式。本文最后针对科技简报文本写作特点，完成科技简报的自动生成功能。

The scientific briefing is a kind of important document in the scientific and technological information, which can provide the reference for the decision makers at all levels to formulate science and technology policies. However, with the advent of the big data era, the traditional scientific and technological information connotation, organizational model and application have undergone different forms of change. How to extract all kinds of valuable information from the vast amount of scientific and technological report data is the challenge that the current researchers are facing. At present, the core of the automatic generation system is text generation, which ignores the text characteristics of the scientific briefing, so the resource utilization rate of the scientific report is not high. Also, the existing text generation technology cannot adequately reflect the rich knowledge network information in the science and technology policy corpus. Based on the above situation, this paper analyzes and experiments the research results of the predecessors, mainly comparing the scientific and scientific briefings published by the Science and Technology Research Institute over the past years with the original scientific and technological policy texts, and researching how to generate short technology from the lengthy scientific and technological policy texts. The analytical techniques include conceptual and relationship labeling, knowledge network construction, network representation learning and text generation. At the end of the paper, these technologies are used to achieve the automatic generation of scientific briefings function.

This paper first analyzes the textual characteristics of science and technology policy categories and automatically labels the concepts and conceptual relationships in the science and technology policy texts according to the classification category of the relationship among concepts. Among them, the concept labeling adopts deep learning method based on RNN+CRF, which realizes the automatic recognition of the concept in the sentence and adds the category label. The relationship labeling mainly analyzes the relationship characteristics between concepts and adopts the SVM active learning classification method to label the relationship between the unmarked concept entity pairs. Experiments show that the technique used in this paper can effectively recognize the concept words in the science and technology policy text and predict the relationship label between concept words.

After realizing the automatic labeling of science and technology policy texts, this paper further studies how to construct a knowledge network through the relationship between concepts. Based on this knowledge network model, the paper adopts a network representation learning basic model that can fuse node semantics, topology structure, and category label information, and also introduces Node2vec algorithm and knowledge representation learning algorithms to improve the basic model. At the same time, this paper analyzes the node representation in the knowledge network with chapter structure. Finally, through the SVM classifier and visualization method, it is proved that the network representation learning method proposed in this paper can more effectively represent the nodes in the knowledge network.

Finally, this paper applies the knowledge network node representation to the automatic generation of chapter structure and content of single-article oriented and multi-article based scientific briefing. For the generation of the scientific briefing text structure, this paper adopts the method of retaining the original text structure or selecting important chapter nodes. For text content generation, this paper studies the two methods of extractive and abstractive generation. Then, this paper focuses on the characteristics of the sicientific briefing text writing and completes the automatic generation of the scientific briefing.

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自改革开放以来，我国新的科技政策层出不穷，这些政策在不同层级政府、不同地区之间进行扩散，对各级政府的政策行为和科技治理水平的提高起到重要的影响，但是鲜有学者对上述扩散现象进行深入的研究。同时，现有科技政策扩散研究多以定性分析为主，侧重对政策扩散理论框架、制约因素、扩散实例展开研究，缺少对所提理论和模型的系统性验证；少数定量研究也主要是应用基本的统计分析，人工参与较多，缺少对政策内容和属性的自动挖掘，难以精确地提取扩散关系，挖掘内容变化。

基于以上情况，本文在总结前人研究的基础上，针对科技政策扩散特点，重点从结构和语义层面构建了科技政策扩散模型，引入自然语言处理领域的文本分析与计算方法，进行扩散特征的自动提取和政策扩散关系的自动挖掘。

（1）对政策领域有意义字符串发现和政策结构提取技术进行研究。首先，针对科技政策中新词术语较多且长度较长，传统分词效果难以达到分析需求的问题，本文提出了基于规则和信息熵的优化方法，实验表明该方法能有效地划分出科技政策文本中绝大部分有意义字符串。对于政策结构，本文分别提出了组织结构提取和扩展方法。首先利用政策行文特点，并结合词频和TextRank算法提取出政策的组织结构。在此基础上，本文构建了科技政策领域结构词表，并根据结构词表对政策的组织结构进行扩展，最终提取出政策的基本面。

（2）对政策扩散特征表示和政策扩散关系判定技术进行研究。首先，本文从结构和语义两方面对科技政策扩散特征进行了研究，分别提取了组织结构相关性特征、基本面同一性特征、特征词承继特征以及基于Doc2vec的文本相似性特征。在特征提取的基础上，本文选用决策树分类模型，将关系判断转化为分类问题，实现对多个特征进行一体化处理，实验表明，本文构建的多特征分类模型能有效地判定政策扩散关系。

（3）对政策扩散识别技术进行研究。首先，针对同一主题下科技政策扩散情况的分析需求，本文构建了科技政策扩散识别框架，并引入了Ranking SVM模型，融合科技政策扩散特征和文本多样化特征，对模型进行了适应化改进。之后，本文提出了基于排序评分的科技政策排序距离计算方法，寻找使扩散关系成立的最大排序距离，作为扩散识别经验值。然后用这一经验值优化识别模型，实现了检索过程中科技政策扩散对和扩散集的自动计算和输出。实验表明，本文构建的科技政策扩散识别框架能有效地提取出扩散集合，满足了用户对某一主题下的科技政策扩散关系挖掘的分析需求。

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信息技术经过60余年的发展，已经普及到社会生活的各个方面。随着信息技术在各医学各领域的应用，大量数据随之产生。皮肤病是常见病及多发病，相关的病症种类多达一千多种，病历数据具有巨大的价值，其语义知识点可用于临床辅助诊疗和健康管理。目前全国皮肤科诊疗室面临着等待时间长、就医用药难、医师诊疗不准等多重问题，皮肤科医师迫切需要一种可以自动化推荐的计算机辅助诊疗工具，以辅助决策和智能医疗诊断。本文提出一种基于蒙特卡罗算法快速识别皮肤病诊疗依据知识点形成皮肤病诊疗最短路径的方法，用于给医师提供下一步最优推荐。

本文在对皮肤病病历结构及内容系统分析及总结归纳的基础上，以《皮肤科诊疗常规》为诊疗依据判定基础，结合蒙特卡罗算法特点及优势，提出了一套以病历诊疗为数据集、以诊疗依据提取与结构化为研究对象、生成皮肤病诊疗路径并基于蒙特卡罗算法计算训练出最短化方案，并通过实验研究验证该方案的可行性。本文研究重点在于如何通过对皮肤病传统的诊疗方法进行分析建模，形成一个能适应于蒙特卡罗算法进行计算的矩阵，如何根据病历及诊疗手册的结构与内容的对应关系提取出皮肤病诊疗依据，如何应用蒙特卡罗算法模拟计算、调参生成皮肤病诊疗最短路径，为诊疗提供支持。

本文的研究工作具体如下：分析皮肤病病历及诊疗手册的文本特征，对文本语义与结构信息进行深入挖掘，从中提取诊疗依据知识点的语义集合。基于文本分析方法模型和机器学习技术，形成能适应于蒙特卡罗算法计算的矩阵，构建出皮肤病诊疗模型。基于蒙特卡罗算法，探索并实现诊疗过程表示及结构化生成、诊疗路径计算与最短化处理关键技术，计算出皮肤病诊疗的最短路径。最后，通过实验论证了上述方法的有效性，可应用于下一步最优诊疗依据推荐。

Information technology has spread to society as a result of the development of more than 60 years. A large amount of data is generated with information technology applied in the field of medical science. Dermatosis is common and frequently-occurring, and there are more than 1,000 kinds of dermatoses now. Medical record data is of great value and its semantic knowledge points can be used for clinical assisted diagnosis and health management. At present, the national dermatology clinics face many problems such as long waiting time, difficulty in medical medicine, and inaccuracy in doctors' diagnosis. Thus, dermatologists need a computer-aided diagnosis tool urgently that can be recommended automatically to assist decision-making and intelligent medical diagnosis. This paper proposes a method to quickly extract diagnosis knowledge points to identify the shortest path of dermatological diagnosis and treatment based on Monte Carlo algorithms, which can be used to provide doctors with the recommendation for the next step.

This paper proposes a scheme for calculating the shortest path of dermatology diagnosis and treatment based on Monte Carlo algorithms after systematic analysis and summary of the structure and content of dermatology medical records. It takes the Routine of Dermatology Diagnosis And Treatment as the basis of diagnosis and treatment, the advantages of Monte Carlo algorithms, and the data set of diagnosis and treatment of dermatology to extract and structuralize of diagnosis knowledge points. Thus the shortest scheme is proposed, and the feasibility of the scheme is verified by experimental study. The focus of this paper is how to model traditional diagnosis and treatment methods to form a large matrix that can be adapted to Monte Carlo algorithms, how to extract the knowledge points of dermatology diagnosis and treatment corresponding to the structure and content of medical records and the Routines of Dermatology Diagnosis and Treatment, and how to adjust the parameters and calculate the shortest path of dermatosis diagnosis and treatment based on Monte Carlo algorithms.

The research work can be expressed as follows: first, analyzing the text characteristics of dermatological medical records and the Routines of Dermatology Diagnosis and Treatment. Based on the modeling of text structure, the knowledge points of diagnosis and treatment through automatic rule extraction are realized. Then, a large matrix suitable for Monte Carlo algorithms is formed on the basis of text analysis method model and machine learning technology, and a dermatological diagnosis and treatment model is constructed. After the diagnosis and treatment processes are represented and structured, Monte Carlo algorithms are used to evolve the diagnosis and treatment paths to calculate the shortest path of dermatological diagnosis and treatment. Finally, the effectiveness of the above methods is demonstrated by experiments, and the recommended system for the optimal diagnosis and treatment of dermatological intelligent diagnosis and treatment is designed and implemented.

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本文主要针对现有技术侦测研究中缺乏先进技术侦测综合模型的问题，利用领域科学文献对先进技术侦测的关键技术进行研究。经过调研发现，在先进技术侦测中，技术点挖掘及其先进性特征在文本中的体现是构建合理有效的先进技术侦测模型的重要任务。因此，本文首先根据先进技术侦测思想方法构建起一个包含领域潜在技术点挖掘和领域潜在先进技术挖掘及其特征发现的侦测模型，之后，对领域先进技术特征建立融合模型以完善侦测模型设计。与此同时，建立针对技术先进性评价的指标体系，最后，在多个技术领域对初始模型进行实验，将实验结果与评价指标进行对比，以优化先进技术侦测模型。具体研究内容有以下几点：

首先，本文探讨了不同类型科学文献资源在技术点获取中的特点并根据文献特点制定了相应的技术词获取策略，并且多源文献的特点也为先进技术文本特征的选取提供了依据。同时，也利用科学文献资源建立了领域知识库，领域知识库的概念结构将帮助后续研究更好地挖掘先进性文本特征。本部分提出了针对技术点特点的TFIDFC-value技术字串提取方法，实验证明该方法具有一定有效性。通过该方法获取的领域技术点，将作为领域先进技术挖掘的基本技术词和先进性评价的部分对象。

其次，本文选取了技术生命周期、领域技术主题演化、领域科技文本术语、领域专利布局四方面以提取技术先进性文本特征，并假设先进技术位于技术生命周期的萌芽期和成长期，出现在领域技术演化的新主题、领域项目文本中的新术语和领域内大公司的非主流专利布局中，并根据假设和基本技术词，提取可能具有先进性的候选技术词，扩大了技术词获取范围。之后，本文根据相关研究总结归纳出技术先进性评价指标体系，基于此前提取的技术文本特征信息进行融合，融合基于技术成熟度、技术知识扩散等理论。指标体系将用于领域先进技术侦测，与先进技术特征挖掘共同构成了初始先进技术侦测模型。

最后，本文选取自动驾驶汽车和物联网领域作为先进技术侦测模型的回溯实验对象，实验证明初始先进技术侦测模型有效，并根据回溯实验结果从提升技术点专业性和单元性角度出发进行侦测模型改进，并将改进后的回溯实验结果与原结果比较分析，实验结果表明，改进后的模型一定程度上提升了排名靠前的候选技术点的先进性侦测准确度。

this paper mainly discusses the key technologies of advanced technology detection in the field of scientific literature. through research and development, it is found that in advanced technology detection, technology point mining, as well as the embodiment of its advanced characteristics in the text, is an important task to build a reasonable and effective advanced technology detection model. therefore, this paper first constructs a detection model including domain potential technology point mining, domain potential advanced technology mining, and feature discovery based on the advanced technology detection theories and methodologies, and establishes a fusion model for domain advanced technology features to improve the initial model design. at the same time, an index system for the evaluation of technological advancement is established. finally, the initial model is tested in many technical fields, and the experimental results are compared with the evaluation index to optimize the detection model of advanced technology. specific research contents are as follows:

firstly, this paper discusses the characteristics of different types of scientific literature resources in the acquisition of technical points, and formulates corresponding acquisition strategies of technical terms based on the characteristics of the literature, and the characteristics of multi-source literature also provide a basis for the selection on advanced technical text features. at the same time, the domain knowledge base is established by using scientific literature resources. the conceptual structure of the domain knowledge base will help follow-up research that further excavates advanced text features. in this part, a tfidfc-value string extraction method based on the characteristics of technical points is proposed. experiments show that the method is effective. the domain technology points obtained by this method will be regarded as the basic technical terms of domain advanced technology mining and part of the of advanced evaluation.

secondly, this paper selects four aspects of technology life cycle, domain technology theme evolution, domain technology text terminology, and domain patent layout to extract the text characteristics of technology advancement, and assumes that advanced technology lies in the germination and growth of technology life cycle, new topics of domain technology evolution, new terminology of domain project text and non-mainstream specialty of large companies in the domain. according to hypothesis and basic technical words, candidate technical words which may be advanced are extracted in the favorable layout, which enlarges the scope of technical words acquisition. then, according to the relevant research, this paper summarizes the evaluation index system of technological advancement. based on the feature information extracted before, it fuses the theory of technological maturity and diffusion of technological knowledge. the index system will be used in the field of advanced technology detection, and together with the mining of advanced technology features, it will constitute the initial advanced technology detection model.

finally, this paper chooses self-driving automobile and internet of things as the backtracking experimental of advanced technology detection model. experiments show that the initial advanced technology detection model is effective, and based on the backtracking results, we improve the detection mode from the perspective of enhancing the expertise and unit nature of the technology points, as well as comparing the improved backtracking results with the original results. to some extent, the improved model improves the advanced detection accuracy of the top candidate technology points.

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近年来，文本生成是自然语言处理领域（Natural Language Processing）一项极具挑战的任务，在解决短文本生成和诗歌生成等方面都取得了不错的进展，但由于当文本变长会造成信息丢失、误差传递和错误偏移等问题，因此在长文本生成上的研究还处于初步阶段，特别是中文长文本生成，而政府公文的生成又是中文长文本生成中特殊的一种。政府公文是我国传达政治任务、表达政治观点以及记录历史事件的特殊文化遗产，有着独特的行文思路和措辞特点，其生成任务所面临的难点与长文本生成有诸多共通之处，都希望生成具有用词多样性（wording diversity）和主题一致性（thematic consistency）的文本。用词多样指句中使用了多样化的词语来表词达意，而不是重复地使用单调的字或词；主题一致指文本句与句之间和句子内部词与词之间阐述的为同一主题。

随着深度学习方法的普及，在文本生成中seq2seq是一种常用的高质量文本生成框架。VAE的引入可以使得seq2seq的生成过程更具多样性，同时学者们发现将生成条件引入VAE中构成CVAE，可以进一步提高句子的内部主题一致性和句子用词多样性。在近期的工作中，关键词也被证实可以作为中间的生成结果来进一步提高句子与句子之间的主题一致性。

虽然CVAEs等已被证实可以用来进行文本生成，但是它们的生成指向性不足，并且不能很好地保证主题一致性以及生成更加多样化的用词。本文试图通过加入类似写作提纲的关键词得到Key-CVAE，使得模型在生成中文政府公文的过程中不仅可以考虑词和词的主题一致性还能进一步优化句子与句子之间的主题一致性。

实验表明，本文模型Key-CVAE不仅在本文构建的政府公文数据集上在篇章和句中主题一致性上取得了高于预期的效果，并且在一系列对比实验中验证了关键词和CVAE的结合不仅加强了CVAE的主题一致性，还保持了用词多样性的性能，同时验证了训练数据集的多样性对模型生成结果的影响。目前，虽然长文本生成技术在中文任务上只是初期探索阶段，但本文引入的Key-CVAE模型具有很好的参考研究价值，为以后长文本生成任务的研究提供了新的思路。

Text generation is a challenging task in Natural Language Processing(NLP). Although text generation has achieved success in many fields such as Short-text generation and Poetry generation. But, when the text becomes longer, it will cause problems such as information loss, error transmission, error migration, etc. Therefore, the research on Long-text generation is still in its preliminary stage, especially in the Chinese Long-text generation, such as the Government document generation which is a special kind of Long-text generation. Government document is a unique cultural heritage with its special use and combination of words. Aiming to publish political tasks, express political views and note historical events. However, the challenges government documents face have much in common with traditional texts, like wording diversity and thematic consistency. Wording diversity highlights the type of words used and thematic consistency emphasized the consistency of theme between sentences and words.

With the popularity of deep learning methods, Seq2Seq is a commonly used and a high-quality text generation framework in text generation. The use of VAE can make the generation process of seq2seq more diverse. Scholars have also found that the generation conditions of CVAE can further improve the VAE’s text generation in internal theme consistency and wording diversity. In recent work, keywords serve as intermediate generation results have also been shown can further improve the topical consistency between sentence and sentence.

Although CVAEs have been proven to be useful for text generation, but their generation is not sufficiently directed. This paper attempts to propose the model: keyword-enhanced conditional variation autoencoder (Key-CVAE) to solve the problem of Chinese government document generation by adding the keywords as writing outline in the consistency of theme between sentences and words.

Experiments have shown that the model Key-CVAE not only achieves higher-than-expected effect on the theme consistency in the government document data set constructed in this paper, but also proved that the combination of keywords and CVAE not only enhanced the theme consistency of the CVAE model, but also maintained the performance of it’s wording diversity, and verified the diversity of the training data set have an impact on the model generation, in a series of comparative experiments. Although Long-text generation is in the preliminary stage in chinese tasks, but the Key-CVAE model introduced in this paper has reference research value which provides a new idea for the research of Long-text generation tasks.

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无论在考试还是在日常生活中，英语写作都是中国学生不可回避的难题。目前，市面上虽然存在包括书籍与网站在内的各种英语写作教学资源，但这些资源的作用相对有限。相关书籍只是理论和语料资源的搜集整合；而写作辅助系统为学生的文章提供分数判定和错误批改，并没有结合学生的写作水平和写作目的针对性地在写后阶段帮助学生获得写作能力的提升。

英语写作教学理论繁多，在众多英语写作教学理论中，让学生通过不断改写文章来提升写作能力是广受认可的一种方案。本系统根据此教学理念设计。为了得到适合每个学生的文章改写方向，本系统充分考量每个学生的写作记录，设计并实现了一种英语写作知识点推荐策略。该策略在运行时可以修正自己的反馈以适应对象的变化。对于英语写作教学，该策略的目的是为学生推荐出其最需要学习的内容以提升其写作能力，本文将这些内容定义为学生使用次数较少而母语者使用次数较多的知识点。

对于中国学生在英语写作时遇到的无词可用、难以连词成句、表达不够地道等问题，本文提出了解决方案并对方案进行了验证。使用系统时，学生输入一篇自己的习作，系统对学生该篇习作和学生的写作历史进行单词、搭配、短语和句型四个维度英语知识点使用频率的计算，对比学生文章与范文中各个知识点的使用频率得出推荐的知识点。在单词推荐模块，为学生推荐使用频率小于同主题范文的单词集合。在搭配、语块和句型推荐模块，对于学生没有使用过的知识点，为其推荐同主题范文中使用频率最高的知识点；对于学生使用过的知识点，为其推荐同主题范文中使用频率/学生使用频率最高的知识点。当学生历史文章数量不足时，通过级别判定和主题约束模块来选取替代文章，使用中国英语学习者语料（Chinese Learner English Corpus，CLEC）作为学生历史文章的替代语料。

测试显示，本系统推荐的知识点可以有效提升写作者的写作表现，知识点的有用性得到了被试者的一致认可。在系统测评时，首先邀请五位英语水平较高的人员进行了英语作文写作。让五位受邀者使用系统改写自己的文章，对改写前后的五篇文章分别进行了专家评分和计算机自动评分，并邀请写作者对系统推荐的知识点做了打分。结果显示，在采纳系统推荐的知识点进行文章修改后，人工评分平均提高了6.3%（满分9分制，平均提高0.568分），机器评分平均提高了0.5%（满分100分制，平均提高0.5分）。在对推荐的知识点进行人工评分时，在满分5分制下，单词推荐结果的平均人工评分为3.65，搭配推荐结果的平均人工评分为2.3，语块推荐结果的平均人工评分为3.63，句型推荐结果的平均人工评分为2.8。为了验证系统对英语水平一般的英语写作者的作用，笔者从CLEC语料库中随机抽取5篇文章，并邀请五位学生对这五篇文章进行严格基于系统推荐的知识点的改写，在修改后，人工评分平均提高了7.4%（满分9分制，平均提高0.67分），机器评分平均提高了8.5%（满分100分制，平均提高8.5分）。

english writing is a major obstacle for chinese students either in exams or in daily life. although various books and websites concerning english writing exist, most of the books are simply the display of corpus resources while the auxiliary websites do nothing more than examing and grading student`s articles. these tools lack the individualized writing guidance which is key to the advance of students` writing ability.

among diverse teaching theories of english writing, one of the most recognized is improvement through rewriting, based on which this system is designed and developed. to get the suitable rewriting direction for each student, the system provides the users with knowledge points of words, collocations, chunks and sentence patterns. this system adjusts its feedback according to the . the main goal of an intelligent auxiliary writing platform is to boost students` writing ability through recommending suitable knowledge points. the knowledge points recommended in this project are those which chinese students rarely use while native writers use a lot.

this system aims to tackle the common problems chinese students have such as lost in words, incapable of connecting words into strong expressions and idiomatically insufficient. this thesis will give solution to these problems and verify the system`s effect. to use the system, students need to input an article, the system will then calculate and compare the usage frequency of different words, collocations, chunks and sentence patterns in user`s articles with that in native writer`s articles to decide which ones to recommend. when recommending words, the system picks those which students use less than native writers. when collocations, chunks and sentence patterns are selected, the system divides the strategy into two scenarios. if a user has never used certain knowledge points, those knowledge points who have the largest usage frequency in model essays are selected. for those knowledge points which have been used before by the user, the system calculates the value of usage frequency in model articles divided by usage frequency in students` articles and the knowledge point with the largest value is recommended. when there are not enough history articles of a user, the system employs level determination module and genre definition module on chinese learner english corpus (clec) for substitution.

the system is proved valid in promoting users`writing ability and knowledge points recommended are approved by the users. evaluation was done first by tracking the artificial scores (given by two english experts) and machine scores (given by pigaiwang, a website embedded with grading module) of five articles written by students. results show that after using the system, artificial scores increase by 6.3% (rose by 0.5 points of a possible 9) on average, machine scores increase by 0.5% (rose by 0.5 points of a possible 100) on average. when asked to evaluate the knowledge points recommended by the system with full mark of 5, the five writers scored the word recommendation module of 3.65, the collocation recommendation module of 2.3, the chunk recommendation module of and the sentence pattern module of 2.8. due to the fact that five writers invited by the author generally have high english writing levels. in order to test the system`s effect on average students, the author randomly extracted five clec articles and invite five students to rewrite them only using the knowledge points recommended by the system. after modification, artificial scores increase by 7.4% (rose by 0.67 points of a possible 9), machine scores increase by 8.5% (rose by 8.5 points of a possible 100).

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古籍是辛亥革命以前传抄或刻印的历史典籍等资源的统称，具有较高的文物价值和 文化意义。但因年代久远，善本难存。为了恢复古籍的原本样貌，古籍工作者需要进 行辑佚、校勘、注释、标点等整理工作，以探求古籍原本样貌，便于后人阅读研究。

为了实现古籍资源共享，数字化是必由之路。如何借助先进的信息技术，提升古籍 整理效率，解决数字化过程中存在的问题是当务之急。通过分析古籍整理的研究现状， 可将问题总结为如下四点：一、古籍整理缺少功能完整、流程完善的开放平台；二、 缺乏统一规范的整理流程，整理工作欠缺指导；三、仅重视整理的结果，丢失整理的 过程信息。四、专业性较强的整理工作缺少专家参与，整理质量参差不齐。 为解决上述问题，以提供便捷、完整、高效的古籍整理系统为目标，结合古籍整理 的特点和原则，笔者创新性地提出了重视古籍整理过程的思路，并完成了多层次、可 追溯的古籍整理平台产品设计，为整理者提供了高效的工作环境。平台的优势可总结 如下：一、包含完整的工作流程，平台将版本选择、文本录入、内容整理等重点工作 囊括在内。在系统的指引下，整理者可通过一个平台完成整理任务，减少不同工具之 间的切换。二、重视整理过程，将系统分成不同的工作层次，借助富信息的设计，保 存每层的校改信息，根据存储的数据追溯整理过程，出现问题便于定位，及时更改， 也为研究提供了支持。三、区分专家和普通整理者角色，分别匹配不同的整理任务， 确保参与者能胜任整理工作，产出符合要求的整理成果；同时，系统分为多个层次， 可在每个层次审查整理结果，保证整理质量。

在北京大学儒藏古籍整理专家的指导下，笔者通过整理工作的典型场景应用范例， 对本研究设计的整理平台进行了验证。工作成果得到了古籍专家的肯定，证明了富信 息整理平台可以为古籍整理工作提供便利，提高整理结果的可信度。 

 

Ancient books refer to the historical books written or published before the 1911 Revolution. Ancient books are the carriers of Chinese culture. Hundreds of years has passed since these books first came out, so they are inevitably suffered loss. To restore the original appearance of these historical books for reading and researching, it is necessary for the specialists to collate, add punctuation and notes, etc. 

On the purpose of widely sharing resources of ancient books, digitalization is the only way. In the past, scholars continually did research work with outdated tools. But nowadays, information technology brings more possibility to collation work. The collation work for historical books is developing through time. Though computer-aided collation systems help improve the efficiency of the work, there are still many problems waiting to be solved in collation practice as following listed:

Firstly, lack of open and integrated platform for collation work. Secondly, deficient in standard workflow and guidance. Thirdly, loss high value information due to the emphasis on the work result rather than the work process, workflow can not be traced back. Finally yet importantly, quality issues on collation work. 

To solve the problems mentioned above, the author proposed a novel idea of valuing collation process. Under the guidance of this idea, the paper designed an ancient books collation system with the following advantages:

Firstly, the system guides users to finish the whole process of collation work. Secondly, the system is designed by the guidance of emphasis on collation process, using XML file to record the data of collation process, which makes tracing the workflow back possible. Thirdly, the design of both user-work differentiated and multiple verification based on multi-layer design offers a guarantee of quality.

This paper has designed a specific collation scenario to validate the design of Information-Rich Ancient Books Collation System. Experts from the Ru Cang Compiling and Editing Center of Peking University had given their recognition. The interviews proved that this study and design could effectively lighten the burdens of collation work and improve the working efficiency to this field. 

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随着互联网的发展，公文已不仅用于政府部门内部传达信息，还开始融入人们的日常生活中，越来越多人通过“学习强国”APP、各类新闻APP的政治板块以及各地政府官网阅读和学习公文。高效的公文阅读不仅能够使公务员更快、更有效地进行公务的上传下达活动，还能使大众快速学习和领会到公文所传达的信息。然而公文的用语并不是人们日常生活中常常使用的口语，而是政府部门日常办公使用的书面语，其特有的准确性、简要性、平实性和规范性等特点导致公文文本的遣词造句都非常的严谨，这样一来公文对读者的教育文化程度和阅读理解能力都有相应的要求。为了让大多数人更容易读懂公文，需要建立有效的公文阅读平台来辅助读者阅读和理解公文。
笔者调研发现，公文阅读有以下两个问题没有得到有效解决：一是读者不易从公文长句中提取关键信息。普通人日常生活中最常用的句子的长度在2~15字左右，而公文句子的平均长度为40~50个汉字，主要是因为公文句子包含的语块较多，例如“社会主义核心价值观”、“两学一做”、“第十九次全国代表大会”等，而且公文中存在包含过多子句的复句。二是当前公文的展示没有突出公文的重点信息。目前公文的电子读物和纸质刊物都以公文的原始文本进行展示，这样的展示方式无法体现公文语言的特色，也不利于读者快速阅读和理解公文。
为解决上述问题，首先笔者从北京大学的规划智库网站上爬取了四千多万字的公文语料作为本文文本处理工作的研究对象，然后结合自然语言处理技术分析公文的文本特征，通过提取公文句子的关键信息和优化公文文章的展示效果，设计并实现了一个公文辅助阅读平台，从以下两个方面来辅助读者阅读和理解公文。一方面，为了提取公文句子的关键信息，本文需要识别出公文句子的语块和主干成分。此外，由于公文句子长度过长，如果用词作为句子的组成单位，读者需要将组成句子的每个词逐个输入脑海组成语块再组成句子，这样会增加读者的短时工作记忆容量，因此本文使用语块来作为句子的组成单位，分别从公文的多字词、组织机构名、四字格短语、文件名和新概念入手，使用规则和统计的方法将这几类语块从句子中抽取出来，其中多字词识别的准确率达到了92.91%、组织机构名识别的准确率为82.88%，组织机构名中的会议名称识别的准确率为87%；公文中复句较多，而且复句常常包含多个子句，因此本文首先将公文的复句拆解为多个子句；同时，本文使用最大熵模型构建句子主干识别模型来识别公文的句子主干，其中例文的主谓宾三个主干成分的准确率都在90%以上。另一方面，本文还利用上述自然语言处理方法分析出的结果对公文进行长句的优化展示研究，以帮助读者更快速有效的理解复杂公文长句。本文首先使用Flask搭建公文辅助阅读平台，将识别出的语块做标记展示，然后通过对段间、句间、子句间做不同的间隔距离处理，来优化公文的展示效果，最后通过抽取文章的章节标题并设计文章的侧边导航，让读者能够快速获得整篇公文的主题脉络，同时提高文章内信息检索的效率。
在本研究的最后，笔者邀请了10位同学完成本文设计的公文阅读理解任务，对比使用公文辅助阅读平台前后的测验成绩结果，结果表明本平台确实对公文阅读起到了一定的帮助。
本文研究和实现的公文辅助阅读平台具有一定的实用价值和研究价值。对于公文读者来说，本文设计的平台有利于提高公文的阅读效率；对于将来从事公文阅读优化工作的工作者来说，自动标记公文的语块和主干成分有利于减少人工标记的工作量。本文对公文语体在技术上的探索和研究，能为将来使用自然语言处理技术研究公文的研究者提供一定的借鉴，也能为其他领域文章的辅助阅读研究做参考。

 

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古籍文献是古籍专家必备的研究资料，也是文化传播的重要载体。传统的古籍研究 以纸质资源为载体，受时间、空间等条件的限制比较多，存在着资源受限、研究效率 不高等问题；而目前的古籍数字化平台在功能、内容呈现模式等方面存在不足，难以 发挥实际作用，要提高研究效率、传播传统文化，需建立有效的研究与阅读平台。 笔者调研发现，以下四个问题暂未得到有效解决。一、对读者来说，阅读古代典籍 时存在着字词、句子篇章和古代常识的理解障碍，而现有的古籍平台在提供古籍资源 时并未解决此问题，不便于传统文化的传播。二、现有的古籍数字化平台未能充分挖 掘文献内容，缺乏文献之间的关系，比如文献之间的引用关系、文献不同版本之间的 关系；更进一步，古籍文献中的人名、地名等专有名词，未与相关材料形成知识网络， 比如缺乏人物与其别名、官职、作品、生平资料等内容的联系。三、未能充分支持研 究者之间的交流与合作，研究者的研究想法与研究成果难以共享。四、缺乏研究支持 工具，比如相似句段发现、对比等。 为解决上述问题，本研究以古籍整理研究理论和方法为依据，结合古籍文献特点， 利用数字化平台的优势，设计了一款多功能古籍协同研究平台，从以下四个方面优化 平台的辅助作用。一、提供字典、词典及参考资料，建立人物传记资料、地理资料等 知识库辅助阅读，并提供注释、翻译等内容解决阅读障碍。二、根据古籍文献特点， 提供专名标注、引书挖掘等数据加工工具，在计算机辅助的情况下，建立文献知识之 间的联系，形成知识网络；三、首先以“研究项目”的资源组织形式，构建研究课题、 研究文献、研究者之间的联系；然后，通过在线批注与编辑功能，实现在线交流与协 作研究；四、集成辅助工具支持研究工作，提高研究效率，如相似句段、文字对比、 典籍引用、引用挖掘等。 最后，本研究以邀请古籍专家试用的方式，以古籍研究实例验证了设计方案的有效 性和合理性。验证结果表明本文提供的协同研究环境与文献标注等工具有效辅助研究 者开展研究工作，提高研究效率。
 
 

Ancient books are essential research materials for experts and important carriers of cultural transmission. Traditional research resources are mainly paper materials, which are limited by time and space, thus experts are confronted with limited research materials and low research efficiency. In order to solve these problems, digital platforms appear and play significant roles in ancient books research, but many shortcomings exist in these systems in terms of functions and content presentation. In this context, this paper aims to design a platform providing experts in ancient books research with a collaborative online research environment, and ordinary readers with a user-friendly reading environment. By studying existing platforms and experts, the author finds out that the following four problems have not been effectively resolved. First, language barriers and lack of ancient common sense are major obstacles to read ancient texts for modern Chinese people. However, current platforms have not solved these problems when providing resources and functions, so they cannot popularize traditional culture with significant effect. Second, the existing platforms fail to fully exploit the content of the literature or link cited documents or different versions of the literature. Furthermore, these platforms are not able to form a knowledge network to connect the proper names such as the names of people and places in the ancient books within related contents. Third, due to the lack of collaborative online research environment, researchers are unable to share research ideas and results with other people. Fourth, there are no research support tools such as similar segments and citation marks. In order to solve the above problems, based on the theory and methods of ancient books research, combining the characteristics of ancient works, and taking advantages of digitalization, this study designs a multifunctional platform for ancient books collaborative research, which plays an auxiliary role in the following four aspects. First, the platform establishes a knowledge base of biographical and geographic materials to help reading and provides annotations, translation and other content to break down reading barriers. Second, considering the characteristics of ancient books, this paper provides data processing tools such as name tagging and quotation mining, and links related contents to establish a knowledge network. Third, the study organizes research topics, literature and researchers by “research project”, and then establishes a communication and collaborative platform with online annotation and editing functions. Fourth, the online system integrates similar segments, text comparison, automatic annotation and other tools to assist research work, improving research efficiency. At last, this paper verifies the validity and feasibility of the design by user testing and case study. According to the feedback of experts in ancient books research, this design complements limited functions of current platforms, helps experts carry out research work and promotes ancient books to the public.
 
 

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      随着国际化进程的不断发展，人们越来越重视外语能力的培养，尤其是在真实情境中的语言运用能力，其中英语写作能力占据了非常重要的位置。但由于国内学生的外语基础较为薄弱，能够实际操练的机会少，且外语写作水平的提升并非一蹴而就，因此写作成为了国内外语学生的弱项，同时也成为了他们焦虑和畏惧的对象。
      随着在线学习的日益普及和游戏化概念的推广，游戏化学习的方法越来越多地应用在各类在线英语学习系统中。但现有的在线写作学习系统仅停留在写作练习本身，没有考虑到学生在写作学习过程中的心理状态与变化，既无法对他们起到激励作用，也无法缓解他们的焦虑感。
      本文基于游戏化写作学习理论、动机激励理论和焦虑理论，分析了现有相关产品在游戏化设计上的优势与不足，针对目标学生群体的心理需求和现有在线学习游戏化设计存在的问题完成大学英语学习平台的游戏化设计。通过游戏、排名、奖励等机制提升学生在学习过程中的获得成就感与满足感，通过明确阶段性学习目标、营造轻松的学习氛围降低学生的焦虑感，通过多维度的学习行为记录与评价机制给予学生更为客观的评价。
      由于系统中涉及的游戏化元素和机制较多且错综复杂，本文从工作成果中选择了比较有代表性的课程地图、积分系统和排行榜三个游戏化模块进行了详细的研究和探讨，针对各个模块提出了不同的设计方案。此外，还简单介绍了本系统中勋章、任务系统以及基于游戏化的学生评价方法的设计思路与方案。
      基于以上工作，本研究选取了40 名非英语专业大学二年级学生进行对照教学实验，通过实验观察以及实验后的问卷调查、数据分析和访谈分别论证了课程地图、积分系统和排行榜模块不同的设计方案，包括帮助学生明确学习目标，给予他们正向激励和成就感，帮助外部动机内化提升学习积极性，以及树立学生的信心、降低写作学习过程中的焦虑感等方面的设计有效性，并针对若干疑难筛选出了各个模块的最佳设计方案。
       本研究中大学英语写作平台游戏化设计填补了在线英语写作游戏化学习方面的空白，有效地提升了学生在写作学习过程中的胜任感、自主感和归属感，缓解了其面对写作学习时恐惧和焦虑的情绪，同时提出了一种基于游戏化的学生评价机制，补足了现有英语游戏化学习设计中缺乏形成性评估机制的短板，对英语写作移动教学和游戏化学习具有一定的参考价值。

     with the continuous internationalization process, people pay more and more attention to the foreign language competence, especially the ability to use the language in real life, in which writing ability plays a crucial role. however, since writing skills could not be improved overnight, most chinese students, who have limited knowledge of english and lack the opportunity to apply it into practice, are frustrated when they write in english.
    with the growing popularity of online learning and the spread of gamification, gamification learning methods are increasingly used in various online english learning systems. unfortunately, existing online writing learning systems only focus on the writing
practice itself but fail to take into account students' everchanging psychological state in the process of writing and learning. consequently, these systems can neither motivate learners nor relieve their anxiety.
    based on gamification writing learning theory, motivation theory and anxiety theory, this paper analyzes the advantages and disadvantages of existing competing products and its counterparts in terms of their gamification design, and aims to design an english learning platform which caters for the psychological needs of university students. learners' sense of achievement as well as their satisfaction are enhanced by games, ranking, rewards and other mechanisms in the learning process. meanwhile, by illustrating learning ives in various stages and creating a relaxing learning atmosphere, they would experience less anxiety.
moreover, students would receive more ive evaluation which is based on multidimensional learning behavior.
    because of the complexity of numerous gamification elements and mechanisms involved in the system, this paper selects three representative component, namely the learning map, score systems and ranking list, for detailed research and discussion. different designs are proposed for each of them. in addition, design ideas and schemes of the medals, task systems and evaluation methods based on gamification are briefly introduced.
    based on the above work, this study selected 40 non-english majors in their second year for the comparative experiments. various designs for learning map, score systems and ranking
list are analyzed with the help of experimental observation, post-experimental questionnaire survey, data analysis and interviews. the designs includes helping students to set clear learning goals, providing motivation as well as a sense of accomplishment, and guiding students to internalize motivations to increase their enthusiasm, as well as building their confidence while reducing their anxiety in writing and learning. the best design for each component is proposed.
    in this study, the gamification design of the college english writing platform fills the blank of the online english writing learning, which effectively enhances students' confidence as well
as their learner autonomy and the sense of belonging in the process of learning how to write. it also alleviates their anxiety and fear during the learning. the evaluation mechanism which is based on gamification compensate the lack of formative assessment in the existing english gamification learning design. it has certain reference value for online english writing learning and gamification learning.

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文本特征的自动量化分析是通过计算机程序实现文本特征的定量评估。文本量化的一大核心是建立一组反映文本特征的指标体系。相较于人工分析，定量分析文本特征更加客观和高效。因此，在西方它已被应用在字母类语言的话语分析、语料库研究等领域。目前，文本特征量化指标的研究多以英文文本为对象，中文文本分析量化指标研究较少。从现有研究来看，中文文本分析主要存在三个问题。第一，现有的中文文本量化指标体系化不足，研究较为单一且不够全面；第二，缺少一个中文文本自动量化分析系统；第三，中文文本量化分析指标体系和量化分析系统的应用价值亟待研究和证明。

基于上述中文文本分析量化指标研究中存在的问题和不足，本研究围绕现代汉语，确立了文本分析量化指标研究、自动量化分析工具的实现、量化指标的应用三大研究主线。第一，在英文文本分析量化指标的研究基础上，以汉语特点和汉语语法为纲，建立了适用于中文文本分析的量化指标体系；第二，以中文文本量化指标体系为基础，依托于自然语言处理技术与中文语言资源，设计并实现了一款中文文本量化分析工具；第三，将中文文本量化分析工具应用于现代汉语文本特征研究和文本分级模型。

本研究建立的中文文本分析量化指标体系聚焦于文本的语言特征，总共包括五个层面：描述性特征层面、汉字层面、词汇层面、句子层面和语篇层面。描述性特征层面包括12个指标；汉字层面包括32个指标；词汇层面包括67个指标；句子层面包括60个指标；语篇层面包括1个指标。整个文本量化指标体系包含的指标共计170余项。本研究以两个应用为例，阐述了中文文本分析量化指标体系和量化分析系统的实用价值。第一，本研究以人教版小学语文教科书课文为例，从汉字、词汇和句子等五个层面，对语料进行了较为全面地统计分析。第二，本研究基于机器学习算法和量化指标体系，构建了文本分级模型，模型的预测准确度高达0.90左右。

研究数据结果显示，随着年级的上升，小学教科书课文的字词量、汉字复杂度、词汇难度和句法复杂度等特征值均呈现上升态势，基本遵循了从简到难的编排特点。然而，课文的用字用词仍存在改进之处。例如低年级课文中出现了较多的非常用字词；部首表收录内容与课文用字的关联度较弱等。这些研究结果已被应用于北京大学俞敬松老师研究小组的相关教学研究中。此外，本研究构建的文本分级模型能参照标准教科书，预测文本的阅读级别，从而被应用于不同阅读级别文本的自动分类和筛选。

automated textual analysis is to analyze text features quantitatively with computer programs. how to build a group of indicators that can reflect text characteristics is one of the core issues of textual analysis. compared with manual analysis of text, quantitative analysis of text is ive and efficient. therefore, it has been applied in the discourse analysis and the research of corpus of alphabetic languages in the western world. currently, most of the studies in automated textual analysis focus on english texts, and the studies in chinese textual analysis are rare. three major shortages can be found in the existing studies in chinese textual analysis. firstly, the existing studies focusing on textual features do not take a systematic and comprehensive approach. secondly, there is no tool available for analyzing chinese texts. thirdly, the practical value of quantitative indices system and the automated tool is still to be researched and validated.

 

to solve the problems of the previous studies related to quantitative indices system mentioned above, and fill the research gap, this research mainly focuses on three issues related to the analysis of modern chinese texts. firstly, in this research, a quantitative indices system is established for chinese textual analysis based on chinese characteristics and chinese grammar. secondly, a tool for the automated analysis of chinese texts based on the indices system is designed and built with the support of natural language processing technology and chinese language resources. furthermore, the tool is used in the analysis of modern chinese texts and the establishment of text leveling models.

 

the quantitative indices system for chinese textual analysis mainly focuses on linguistic features. this system consists of five levels: deive indices, chinese character, words, sentence, and discourse, with 12, 32, 67, 60, and 1 indicator for each level, respectively. in total, the indices system has more than 170 indicators. furthermore, in this research, two applications are used as examples to prove the practical value of quantitative indices system and the automated tool. in the first application, the linguistic features of primary school textbooks, published by the people’s education press, are extracted. the textbooks are analyzed thoroughly on the five levels, including chinese character, words, and sentence. in the second application, the quantitative indices system is integrated with machine learning algorithms, and text leveling models for chinese texts are built. the prediction accuracy of text leveling models is about 0.90.

 

according to the results, many linguistic values related to the features of textbooks such as word count, chinese character complexity, vocabulary level, and syntactic complexity, show an upward trend as the year increases, indicating that in general the texts are simpler for students in lower years, and are more complex for students in higher years. however, there is still room for improvements regarding the use of characters and words. for example, there are lots of uncommon words in the textbooks for students in lower years. besides, the content of the radical table has little connection with the chinese characters used in textbooks. these findings have been used to support the relevant research by jingsong yu research team of peking university. furthermore, the text leveling models can be used to predict the reading level of chinese texts with the reference to the levels of standard textbooks, and therefore, these models can be used for automated classification and selection of chinese reading texts.

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       人类社会进入21世纪以来，科技的飞速发展带动了各个领域的不断进步，与此同时，人们在各个专业化领域的需求也在不断探索和前进。因此，传统的专业领域工具已经无法满足人们的需要。对于医学领域人士（医生、医学生、医学爱好者等）来说，一款高效的医学英语电子词典是他们工作、学习必不可少的好帮手。

       然而，纵观过往的医学英语电子词典产品，大多存在以下三个问题：第一，依附于通用型词典之上，对于医学领域的专业广度和深度拓展不够。词汇和相关内容的搜索展示仍停留在通用词水平，无法为专业人士提供符合他们专业程度的词汇查阅需求。第二，词典功能单一。当前市面上流行的许多医学电子词典都将功能局限在“查词”上，无法为专业医学人士日常所需的文献阅读和论文撰写提供较为便利的解决方案。第三，无法辨别医学用户的专业偏好。目前，多数常用电子词典对于医学用户的专业偏好并没有区分，从而无法从专业的科室角度为用户提供高效的查词体验,也无法为用户提供个性化的搜索和展示。

      为了解决上面三个问题，本研究首先进行了医学英语词汇的构成分析，研究英语词汇的特点，提升词典设计的合理性。然后从医学英语词汇联结的角度入手，研究了UMLS和SNOMED CT两大医学界较为权威的英语词汇系统，从中提取医学英语词汇之间的相互联系。并且，为了解决现存医学数据库中词汇科室不明的问题，本研究采取机器算法与人工校对相结合的方式，利用不同科室的核心词汇表，加上提取的词汇联系网络，将获得的医学词汇数据进行科室分类。最后，本研究结合个性化产品设计的思路，辅之以针对医学用户日常所需的便利功能，如写作助手和阅读助手，为医学领域的专业用户设计了一款个性化的医学英语电子词典。

      为了验证本研究设计的词典的实用性，本研究邀请了来自浙江大学医学院的专家与同学参与了有效性验证。实验表明，本研究设计的电子词典可以有效提高用户查词的效率。此外，词典系统中的辅助功能，如写作、阅读助手等，也帮助用户提高了学习与工作的效率。本研究设计的医学英语电子词典有效地解决了当前医学电子词典中存在的专业化和个性化问题，帮助用户提高了学习和工作效率，对医学英语电子词典的研究与发展有一定参考价值。

  Great changes have appeared in various fields with the rapid development of science and technology since the beginning of the 21st century. At the same time, the needs of human beings in various specialization fields are also being advanced. Therefore, traditional professional learning tools can no longer meet people's needs. For people in the medical field (doctors, medical students, medical enthusiasts, etc.), an efficient professional medical English dictionary is an essential helper for their work and study.

  However, when it comes to the past medical English dictionaries, there are following three problems in the most of these dictionaries: Firstly, these dictionaries are one part of ordinary dictionaries, in which users are difficult to find professional knowledge, especially for professional medical users. Some uncommon knowledge, such as diseases, treatments and so on, also attracted medical users. Secondly, most dictionaries nowadays have a single function of word-retrieval. It is difficult for a simple dictionary to satisfy medical professionals’ daily needs since they have to read and write professional literatures in work and study. Thirdly, all users are treated equally when using those dictionaries, which means that they are difficult to distinguish the professional preferences of medical users, nor is it possible to provide users with efficient word- retrieval and word-display experience.

  In order to solve the three problems above, this study firstly carries on the characteristic analysis of medical English vocabulary to improve the professionalism of dictionary design. Then, from the point of view of medical English vocabulary association, this paper studies the two authoritative English vocabulary systems, UMLS and SNOMED CT, from which the relationship between medical English vocabulary is extracted. Moreover, in order to solve the problem that vocabulary departments in the existing medical database are unclear, this study adopts the way of combining machine algorithm with manual proofreading, uses the core vocabulary of 18 departments, and adds the extracted vocabulary connection network. The obtained medical vocabulary data thus are classified into sections. Finally, combined with the idea of personalized product design, this study designs a personalized medical English dictionary for professional users in the medical field, supplemented by some convenient functions for medical users.

  To verify the practicability of the medical English dictionary designed in this study, experts and students from School of Medicine in Zhejiang University were invited to participate in the verification of validity. The results of experiments show that the dictionary system designed in this study can improve the efficiency of word-retrieval. In addition, other auxiliary functions in the dictionary system, such as writing and reading assistant, also help users to study and work more efficiently. The medical English dictionary designed in this study has solved the specialization and individualization problems existed in the current medical dictionaries, and it also helps users study and work efficiently. What’s more, the study has certain reference value for the further development of the medical English dictionary design. 

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词汇教学是英语教学的重要组成部分。在词汇教学中，教学材料直接影响到学生的学习效果。经过调研，发现虽然目前可使用的英语词汇学习资料丰富多样，但是内容分散，良莠不齐，无法完全满足教师和学生的资源需求。如果能够发挥计算机和互联网技术的优势，对词汇学习材料进行采集和整合，将对英语词汇教学质量的提升有所助益。

为整合英语词汇教学中的资源，本研究广泛地收集词汇学习材料，使用计算机技术对文本进行加工，根据一定逻辑对素材进行组织，建设英语词汇学习知识库。本文研究的关键问题有知识库建设的方法和思路、大规模资源汇总、资源加工存储的规范标准和自动处理程序的设计与实现。

本研究从互联网上采集了大量词汇学习资源，进行汇总，建设资源库。之后在文献分析的基础上构建词汇知识模型，并以此作为整合资源的逻辑依据。词汇知识模型从习得过程和知识内容两个角度出发组织词汇知识，响应了学习者在习得词汇过程中词汇知识需求的动态变化。模型以词汇习得过程为框架，词汇知识为内容，将词汇习得过程分为感知、理解、联想和输出四个阶段，让知识内容聚合到音位、形式、语境、语义、搭配、词源、产出和主题八个维度下。在建设知识库时，根据词汇知识模型设计知识库的结构和资源加工的规范标准，对例句和搭配的自动抽取等关键问题进行研究。最终，本研究整合了通识英语教学中的词汇学习资源，解决了知识库建设中的关键问题，开发了自动处理程序，实现了一定规模的词汇学习知识库。

对于整合后的词汇学习资源，本文进行了客观指标评估、准确率抽样检查和场景检查，证明了知识库能够满足教师和学生的资源需求，帮助改善词汇教学的效果。

本研究的创新之处包括以下三点：1）在文献研究的基础上构建词汇知识模型，根据知识模型从各类词汇学习资料中针对性地提取内容，然后进行整合，保证了内容的丰富性和资源间的关联性，去掉了重复性和低质量的学习材料，是一种语言学习资源建设的新思路；2）知识库将学习资源聚合到不同的维度下，实现了资源的模块化，在应用知识库时，可以根据需求灵活地调用和组织内容；3）本研究在加工资源时，对自动处理程序中的关键问题进行研究，使用自然语言处理等计算机技术提高资源建设的效率。

词汇教学是英语教学的重要组成部分。在词汇教学中，教学材料直接影响到学生的学习效果。经过调研，发现虽然目前可使用的英语词汇学习资料丰富多样，但是内容分散，良莠不齐，无法完全满足教师和学生的资源需求。如果能够发挥计算机和互联网技术的优势，对词汇学习材料进行采集和整合，将对英语词汇教学质量的提升有所助益。

为整合英语词汇教学中的资源，本研究广泛地收集词汇学习材料，使用计算机技术对文本进行加工，根据一定逻辑对素材进行组织，建设英语词汇学习知识库。本文研究的关键问题有知识库建设的方法和思路、大规模资源汇总、资源加工存储的规范标准和自动处理程序的设计与实现。

本研究从互联网上采集了大量词汇学习资源，进行汇总，建设资源库。之后在文献分析的基础上构建词汇知识模型，并以此作为整合资源的逻辑依据。词汇知识模型从习得过程和知识内容两个角度出发组织词汇知识，响应了学习者在习得词汇过程中词汇知识需求的动态变化。模型以词汇习得过程为框架，词汇知识为内容，将词汇习得过程分为感知、理解、联想和输出四个阶段，让知识内容聚合到音位、形式、语境、语义、搭配、词源、产出和主题八个维度下。在建设知识库时，根据词汇知识模型设计知识库的结构和资源加工的规范标准，对例句和搭配的自动抽取等关键问题进行研究。最终，本研究整合了通识英语教学中的词汇学习资源，解决了知识库建设中的关键问题，开发了自动处理程序，实现了一定规模的词汇学习知识库。

对于整合后的词汇学习资源，本文进行了客观指标评估、准确率抽样检查和场景检查，证明了知识库能够满足教师和学生的资源需求，帮助改善词汇教学的效果。

本研究的创新之处包括以下三点：1）在文献研究的基础上构建词汇知识模型，根据知识模型从各类词汇学习资料中针对性地提取内容，然后进行整合，保证了内容的丰富性和资源间的关联性，去掉了重复性和低质量的学习材料，是一种语言学习资源建设的新思路；2）知识库将学习资源聚合到不同的维度下，实现了资源的模块化，在应用知识库时，可以根据需求灵活地调用和组织内容；3）本研究在加工资源时，对自动处理程序中的关键问题进行研究，使用自然语言处理等计算机技术提高资源建设的效率。

vocabulary teaching holds an important position in english language teaching and the materials directly influence the learning effect of students. it was found that although the english vocabulary learning resources currently available are rich and varied after surveying teachers and students, problems do exists with the quality and organization of these materials which fail to fully meet the resource needs of teachers and students. if vocabulary learning materials were collected and integrated with computer and internet technologies, the quality of english vocabulary teaching could be improved.

in order to integrate resources in english vocabulary teaching, the author collected quantities of vocabulary learning materials, applied computer technology to process texts, organized materials according to certain logic, finally realized an english vocabulary learning knowledge base. the key issues in this study are the methods and ideas of knowledge base construction, gathering large-scale resources, standardizing the resource processing and storage, and designing automatic text processing programs.

this study collected a large amount of vocabulary learning resources from the internet based on which a resource library was built. then, after the literature analysis, the author proposed a vocabulary knowledge model as the logical basis for integrating resources. the vocabulary knowledge model organizes vocabulary knowledge from the perspectives of acquisition process and knowledge content, responding to the dynamic changes of vocabulary knowledge needs of learners in the procedure of acquiring vocabulary. the model takes the vocabulary acquisition process as the framework and the vocabulary knowledge as the content. the vocabulary acquisition process is divided into four stages: perception, understanding, association and output. the knowledge content is aggregated into the eight dimensions of phoneme, word form, context, semantic, collocation, topic, source and output. when constructing the knowledge base, the knowledge base structure and the normative standards of resource processing were designed according to the vocabulary knowledge model, and key issues such as automatic extraction of example sentences and collocations were studied. in the end, this study integrated the vocabulary learning resources in general english teaching, solved the key problems in the construction of knowledge base, developed automatic processing programs, successfully built a vocabulary learning knowledge base of a certain scale.

for the integrated vocabulary learning resources, this paper conducted ive index evaluation, accuracy sampling and scene inspection, which proved that the knowledge base could meet the resource needs of teachers and students, improving the effect of vocabulary teaching.

the innovations of the study include the following three points: 1) by extracting and integrating resources from various vocabulary learning materials according to the knowledge model, the study can ensure the richness and connection of the resources in the knowledge base, moreover, remove the repetitive and low-quality learning materials. this is a new way to build language learning resources; 2) the knowledge base aggregates learning resources into different dimensions and realizes the modularization of resources. when applying the knowledge base, the users or applications can flexibly invoked and organized the resources according to their requirements; 3) this paper studied the key issues in the automatic processing program when integrating resources, and used computer technology such as natural language processing to improve the efficiency of resource construction.

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随着我国涉外法务日益频繁，法律英语的重要性毋庸置疑。想学好法律英语需要先了解它的特殊性，根据前人研究，法律英语的特殊性首要体现在法律英语词汇的特殊性。本研究期望借助移动学习的优势，设计一个法律英语词汇学习系统。

经调研，目前的法律英语词汇教学在学习效率方面存在以下问题未能得到有效解决。第一，目标词汇不成体系，未能建立词汇的属性标注和关联关系。学习资源以纸质教材为主，存在学习内容有限等问题；相关词汇学习系统仅将纸质教材中的词表电子化，未能建立词汇的属性标注和关联关系。第二，词汇学习深度不足，没有突出重难点。一般英语学习的基本词汇信息未能满足法律英语词汇学习的深度需求；没有重点设计近义辨析这一教学难点。第三，词汇考查内容不足，复现形式单一。考查内容只包括词汇的基本信息，缺乏法律英语词汇的特殊信息；仅以测试实现词汇复现，形式单一。第四，一般英语的词汇推荐策略不完全适用于法律英语词汇。词汇优先级计算忽视了法律英语词汇高频使用特别术语的特征；近义词学习未能实现动态推荐。

为解决上述问题，本研究在相关词汇教学理论和二语习得理论的指导下，借助移动学习的优势，设计了法律英语词汇学习系统。第一，多维度整合学习资源确立目标词汇资源库，对词汇的词源和部门法等属性信息进行标注、建立近义词汇的关联关系。第二，根据法律英语学习的深度需求，增加构成要件等学习内容，重点设计近义辨析模块。第三，设计多种复习题型考查法律英语词汇的基本信息和特殊信息，多种法律语境复现法律英语词汇。第四，结合法律英语词汇特征和学习者的学习情况，融入初始熟悉度因子综合计算词汇优先级，优化近义词推荐策略。

对于资源建设和词汇推荐策略，本研究邀请专家通过访谈的形式验证了设计的合理性和有效性。对于功能设计部分，通过对学习者进行问卷调查和深度访谈的方式验证了本系统在认知负荷相似的情况下，在学习目标达成方面优于现有的词汇学习系统。

本研究设计的法律英语词汇学习系统抓住了法律英语词汇的特征和教学重难点，发挥移动学习的优势，克服了纸质资源的局限性，作为课堂教学的有益补充，帮助学习者高效学习法律英语词汇。

with the increase of china’s foreign legal affairs, legal english plays a more important role. if students want to learn legal english well, above all, they are supposed to know the specialty of legal english, which mainly reflected by its vocabulary. this study aims to design a legal english vocabulary learning system with the help of mobile learning.

when it comes to the efficient learning of legal english vocabulary, there are still several problems. first of all, learning resources have not been integrated effectively. the number of vocabularies in paper textbooks is limited; current systems either label attribute or make the association of synonyms. secondly, current systems fail to meet the learning depth of legal english vocabulary and to design the discrimination of synonyms as a key point. thirdly, the review content of current systems is unable to meet the review needs of those students who have different learning ives. and the review form of current systems is only based on examinations, which is prone to make students feel boring. fourth, the vocabulary recommendation strategy of current systems is not suitable for legal english vocabulary. the difficulty grading system ignores the influence of the learner's previous english level. in addition, the recommendation of synonyms can’t be adjusted dynamically.

this study tries to solve these problems by following ways. first, this study multi-dimensionally integrates learning resources and labels the attribute of legal english vocabulary to meet the needs of different students at the learning portal. second, this study selects the learning content according to the characteristics of legal english vocabulary, trying to highlight the difficulties of legal english vocabulary teaching. third, this study designs a variety of questions to meet the different review needs of learners and makes learned vocabulary repeat in different legal contexts to stimulate students’ interests. fourth, this study takes students’ former english level into consideration to make an effective learning sequence for legal english vocabularies and different study strategies for synonyms.

this study invites experts to evaluate the resource integration part and vocabulary recommendation part by interviews, which verifies the rationality and effectiveness of the design. in addition, this study conducts an experiment on legal english vocabulary learners by questionnaires and interviews, which shows that the cognitive load of the system designed in this study is similar to that of the existing systems, but the achievement of learning goals is more conductive.

the legal english vocabulary learning system designed in this study captures the characteristics of legal english vocabulary and teaching key points, overcomes the limitations of paper resources by virtue of the advantages of mobile learning, and serves as a useful supplement to in-class teaching, helping students to learn legal english vocabulary efficiently.

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信息时代，合作探究教学模式能够给予学生更充分的思考空间和更丰富的思维训练，逐渐成为教育教学中的热门研究话题。在合作探究教学中，主要由学生小组合作展开讨论学习，教师辅助进行指导。众多教育研究者提出合作探究教学是促进学生思辨能力的有效方式。

然而，该模式在实际教学中面临了一些问题。笔者作为《翻译技术原理与实践》课程的助教，发现目前的合作探究教学存在三个问题：（1）如果缺少学生对彼此研究内容的评判或教师对学生发言观点的评价，难以培养学生思维的严谨性；（2）学生不能积极给予同伴建议或教师未能及时提供启发，将不利于训练学生思维灵活性；（3）若学生之间没有情绪表达沟通或教师未能提供积极的鼓励，容易导致学生的思维自主性难以被触动。

针对上述问题，笔者阐述了在现有合作探究教学模式中引入六顶思考帽理论的必要性，明确了教学研究思路和研究方法，创新性地提出了基于六顶思考帽理论的合作探究教学模式。新模式主要包括：学生借助思考帽讨论流程强化小组讨论中的同伴互助作用，教师利用思考帽进行评价反馈增加小组讨论中的教师辅助引导作用。

为验证新模式的有效性，笔者于2018年3月至6月对北京大学外国语学院的14名学生开展了教学实验，实验类型为单组前后测，前后测数据来源为学生的线上讨论记录和问卷调查，并通过定量分析和定性分析对教学实验前后测数据进行对比，验证基于思考帽理论的合作探究教学方法在实际课堂应用中的效果。

实验结果表明，学生的思辨能力在灵活性、严谨性和自主性方面有提高，在一定程度上证明基于思考帽理论的合作探究教学对学生思辨能力提升有积极效果。该研究对合作探究教学模式进行了探索，为其进一步优化提供了新思路。

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随着全球化的深入，英语写作变得愈发重要。学习者对于提升写作能力的诉求也越来越强烈，由于传统写作教学对写作成果的重视程度远大于其写作过程，写作能力的提升也并非一蹴而就，因此写作成为了众多学生英语学习的弱项。近年来随着在线学习的普及以及过程写作理论、协作学习等理论的推广，越来越多的学者开始重视写作过程，提出了群组讨论和同伴互评等协作学习方式，并将其应用到各类在线英语写作教学和英语写作学习系统中。然而，现有的在线英语写作教学、学习系统并未从英语写作学习的实践出发，群组讨论阶段存在讨论跑题、积极性不足等问题；同伴反馈阶段存在互评质量差，参与意愿低等问题，学生的写作能力提升缓慢。

本文以二语写作理论、协作学习理论、激励理论及结构化研讨方法为依据，分析了现有写作教学模式和竞品在社交化模块上的优势与不足。针对其中存在的问题以及大学生群体的写作需求，结合社交化模块的评价标准，对自适应英语写作系统的社交化模块进行了设计。通过建立结构化的讨论社区，提高讨论的质量，培养学生的思维能力，降低学生在写作过程当中的无助感和焦虑感。通过建立结构化的同伴互评方式，帮助学生建立批改思路，获得多元化、高价值的反馈意见。通过奖励等机制的设计，提升学生在学习过程中的参与感和满足感，激发学生的参与意愿。

由于系统中涉及的社交化机制较多，本文在此不一一详述，拣选两个具有代表性的功能——创意广场和同伴互评进行了研究和探讨，并针对这两个功能提出了不同的设计方案。此外还对系统中其他模块的设计进行了简单的介绍。

基于以上设计，本研究选取了南京某高校20名非英语专业学生进行了教学实验。通过实验观察、数据分析、深度访谈、调查问卷等方式，论证了创意广场和同伴互评设计在提高群组讨论质量，降低学生无助感和焦虑感，培养学生思维能力，提升讨论积极性以及在提高同伴互评的质量，提升学生评判性思维能力、认知能力以及参与意愿等方面大有裨益，并筛选出了这两个功能的最优方案。

本研究中自适应英语写作系统的社交化设计弥补了在线英语写作协作化学习方面的不足。在群组讨论阶段，讨论质量及学生们的思维能力均有所提高，缓解了学生写作时的焦虑和畏难情绪。在同伴互评阶段，提升了互评的质量，满足了学生对于多元化，高价值反馈的需求，提升了学生的能力，激发其参与反馈的意愿，补足了现有英语写作协作化学习设计的短板，对英语写作移动教学和协作化学习有一定的参考价值。

As globalization continues to develop, English writing becomes all the more important, which leads to stronger desire of English learners to improve their writing skills. Traditional teaching of writing, however, emphasizes results over processes, leaving writing skills that cannot be cultivated overnight a weak link for college students. In recent years, with online learning, the process theory of composition and the collaborative learning theory gaining popular, more scholars than before pay attention to the writing process. They put forward collaborative learning approaches such as group discussion and peer review, and also apply these approaches on various online English writing systems. However, existing online English writing systems are not practical. Problems occur such as off-the-topic or poorly motivated discussions, low quality of peer evaluation and low willingness to participate. All these lead to slowly improved writing ability.

Based on L2 writing theory, collaborative learning theory, motivation theory and structured research method, this thesis analyzed common teaching methods of English writing and current teaching products. The author applied standards and contents of the collaborative learning methods and then designed systemic social modules targeting the problems mentioned above and needs of college students for writing. This thesis hopes to improve the quality of discussions and train students' critical thinking via structured discussion groups so as to reduce their helplessness and anxiety in the writing process. Structured peer review and intelligent recommendation of reviewing peers can help students set up the correction ideas and get diversified and valuable feedback. Besides, with rewards and other systems, students’ would be more satisfied and motivated to participate in the discussion.

As there are loads of social mechanisms involved in the system, this thesis was not able to cover all of them and thus selected two core functions for research, namely the creative square and peer review. This thesis includes different design proposals and brief introduction of ideas of designing other modules in the system.

In this thesis, 20 non-English majors from a university in Nanjing were invited in the experiments. Through experimental observation, data analysis, in-depth interview and questionnaires, the author proves creative square and peer review can improve the quality of group discussions and reduce students’ helplessness and anxiety. On top of that, they also help to cultivate students’ critical thinking, motivate students to discuss and promote the quality of peer review and students’ willingness to participate. In the thesis, the best solution of each function was also presented.

In this study, the social design of the adaptive English writing system optimized the process of collaborative learning, improved the quality of discussion, developed the students' critical thinking and relieved the anxiety in the writing process. In the post-writing stage, the quality of mutual evaluation was improved to meet the needs of students to get diversified and valuable feedback. This can inspire students' willingness to provide feedback and make up the weak link of online English teaching of writing, which has certain reference value for the mobile teaching of English writing and cooperative learning.

 

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    与普通英语词汇学习相比，托福词汇学习的特征主要表现为学科性强、学习量大和准备周期短。部分学习者对托福词汇学习存在误解，认为只需了解词汇意思即可，但根据托福考试要求，其中一部分核心高频词需要被转换为积极词汇，即需要在听力、写作和口语中熟练运用。调研表明，已有的背托福词汇APP或微信小程序并不注重积极词汇的训练，使得学习者对词汇的理解仅停留在“阅读”层面。
    本研究始于需求调研，以托福词汇教学理论、积极词汇学习理论、第二语言词汇习得理论为基础，以提高学习者学习效率和学习主动性为目的，设计了一款侧重于托福积极词汇学习的微信小程序。与常规词汇学习APP相比，尝试实现了以下改进：首先，在提高学习者学习效率层面：梳理托福常考学科和意群，通过词以类记的方式促进学习者根据学科和意群进行分类记忆；通过词频分析和归纳，统计出托福考试核心高频词作为积极词汇，包括：听力高频词、写作高频词、口语高频词，供学习者学习；利用语块输入、语境输入、联想输入和多感官输入，促进学习者的积极词汇学习输入；结合托福考试的具体考核方式，定制习题内容，促进学习者的积极词汇学习输出。其次，在提高学习者学习主动性层面：结合ARCS模型和认知负荷理论，在界面设计上从“注意力”、“相关性”、“自信心”三个方面提高学习者学习兴趣；并利用微信小程序可与微信群密切结合的独特优势，通过积分鼓励制和陪伴学习制增强学习者的粘性和活力。
    为验证本设计有效性，武汉某大学55名学习者参与了为期3周的教学实验。通过前测（使用微信小程序学习之前）、后测（使用微信小程序学习之后）和延后测（后测完成一周之后）检测学习者的学习效率，并使用满意度调查问卷检验学习者的学习主动性（注意力、相关性、自信心、满意度）。实验结果表明：本研究设计的托福积极词汇学习微信小程序比现有的托福词汇微信小程序更能显著提高学习者的学习效率，但在提高学习主动性方面还需更多探索。

    Compared with general English vocabulary building, TOEFL vocabulary building is characterized by involving a variety of disciplines, huge workload and comparatively less time for preparation. Some students have misconception on TOEFL vocabulary building, by false believing that to know the Chinese meaning of the words would be sufficient. Nevertheless, according to the test requirements for TOEFL, students should have the capacity to understand some TOEFL words in listening, writing and speaking tests. Survey shows most apps or Wechat Applets cannot help students to effectively build their active vocabulary and vocabulary building is meant for reading purpose only. 
    Initiating from a requirement survey, this study applies the following theories regarding TOEFL Vocabulary Coaching, Active Vocabulary Building and the Second Language Acquisition in the design of this TOEFL Active Vocabulary building Wechat Applet to improve the learners’ learning efficiency and learning attitude. In comparison with existing alternatives, to begin with, this Wechat Applet attempts to improve learning efficiency in the following ways: 1. Classify TOEFL words according to different disciplines and meaning groups to enable memorization through classified words. 2. Generate a core TOEFL Active Vocabulary word list using a frequency analysis, including most frequently used words for listening, speaking and writing. 3. Combine the input of lexical chunks, the input of context, the input of association and the input of multi-sensory integration to improve students’ learning efficiency in terms of TOEFL Active Vocabulary input. 3. Tailor the testing methods according to the specific requirements of TOEFL examination to enhance the learners’ TOEFL Active Vocabulary output. Subsequently, the current Wechat Applet uses the ARCS model and the Cognitive load theory to improve students’ learning attitude in three clear areas: attention, relevance and confidence. This study also engages students through motivational systems of points-accumulation as well as the use of WeChat groups. 
    An empirical experiment that lasted for three weeks was conducted in a Wuhan university. 55 first grade students tested the validity of the current Wechat Applet. The learning efficiency of the students were examined using three tests, namely, a pre-test(before using the Wechat Applet), post-test(after using the Wechat Applet) and delayed-test(one week after the post-test), whilst their learning attitudes were investigated through use of a satisfaction questionnaire. In conclusion, the Wechat Applet studied here can significantly improve students’ learning efficiency compared with existing apps in the market; nonetheless, further exploration should be made into improving students’ learning attitude.

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目前，国内翻译研究多集中于译者、译作和翻译策略等方面，审校相关研究相对较少，而对中文版图书的出版审校研究则更为罕见。笔者在翻译《培养小极客》（Bringing Up Geeks）一书的过程中，深入了解了出版社的审校流程，并发现目前部分出版社在出版中文版图书时，会在原有的内部专业审校基础上，增加目标读者审校的环节，从而弥补专业审校的不足，提高中文版图书编校质量，提升读者对中文版图书的阅读体验。

因此，本文基于对《培养小极客》的翻译和审校过程中出现的具体案例，以目标读者审校为研究对象，以明确目标读者审校的价值为研究目的，首先通过理论与现实结合的方法对审校者多样化与读者“反馈提前化”的可能性进行了论证，并从理论角度分析了目标读者在审校中可能起到的作用，从而明确了目标读者参与中文版图书审校的可行性；其次从审校专业能力、审校目的、审校标准或规范和审校方式四个维度对专业编校人员和目标读者进行对比分析，并通过对《培养小极客》中具体案例的分析，详细探讨了目标读者审校在句义不明、词义不明、文化差异和表达优化四类问题上所发挥的作用；最后结合对专业审校局限性的分析，明确了目标读者审校的作用，即（1）帮助解决专业审校忽视的语义不明问题，增强译本的可理解性；（2）润色文本表达，提升译本的可读性；（3）帮助解决专业审校忽视的文化差异问题，并提出对应的多样化解决方案，提升译本的丰富性。

本文证明，在中文版图书出版过程中，目标读者审校是对专业审校的一个有效补充，将其纳入出版社的审校流程，在弥补专业审校的局限性和盲点、提高图书编校质量、增强图书对读者的吸引力和说服力等方面均有一定意义。

Currently, domestic translation studies mostly focus on translators, translation works, and translation strategies, while researches on review and publishing process are relatively rare. After finishing the translation of Bringing Up Geeks written by American writer Marybeth Hicks, the author of this paper pays more attention to the review and publishing process and finds that the publisher’s review contains not only internal editor’s review but also external target reader’s review. The latter is to make up for the limitations and shed light on the blind spots of the editor’s review, improve the quality of translation, as well as enhance readers’ reading experience.

Combining qualitative analysis with quantitative analysis, this paper analyzes the differences between editor’s review and target reader’s review and systematically clarifies the functions of target reader’s review based on the specific problems occurring during the review and publishing process of Bringing Up Geeks. The analysis shows that the editor’s review and the target reader’s review differ in skills, purposes, standards and methods. Also, the research finds that the target reader’s review can complement the editor’s review in four aspects occurring in the translation: ambiguity of sentences, ambiguity of words, cultural differences and expression optimization, even though the target reader may present wrong or unnecessary suggestions due to subjective factors.

In conclusion, this paper advises that translation revision is a critical process in the publication of translated books. Target reader’s review, as a relatively new mode of review among domestic publishers, presents interesting interactions between the translator, editor and reader. It is an effective supplement to the editor’s review and therefore deserves more attention from both publishers and researchers.

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翻译不仅是语言的解码和编码过程，也是跨文化交际的过程。因而回译对原文的还原和回归，不仅是语言上的还原，也包含文化的还原。本文基于美国作者约书亚•葛以嘉（Joshua Goldstein）《伶界大王：1870-1937年京剧再造时期的演员与观众》（Drama Kings: Players and Publics in the Re-creation of Peking Opera，1870-1937）一书的翻译实践，探讨了原文中存在的文化英译问题，以及在回译时针对不同内容所采取的还原策略。

在对回译研究、文化还原和京剧翻译研究做了简要回顾后，笔者首先分析作者在写作过程中对京剧文化的英译特点，探讨其带来的翻译难点。笔者发现，作者在英译京剧文化术语和专有名词时大量地使用音译和拼音注释。笔者以为这一翻译方式较好地保留了中国文化的异域特色，且相应的解释说明有助于读者了解文化概念的内涵，但同时作者的英译也存在不够准确或不够恰当的情况。其次笔者指出了原文术语模糊翻译、一词多义、人名音译错误和引用来源多元等现象给回译实践造成的难点。

对于本书第二章已有的译文，笔者分析了其存在的问题，主要是词汇还原不准确、还原有误和引文未实现至译的情况。针对这些问题，笔者皆给出了自己的思考及认为更恰当的译法。接下来笔者总结归纳了在回译实践中针对不同情况所采取的还原策略，主要从对词汇和引文的还原两个角度出发。对词汇的还原方法有经过仔细详尽的查证后给出译法、省去不译原文中对于中文读者来说冗余的解释、通过添加文内注释或脚注的形式增添必要的解释说明或背景知识以增强译文的可读性。针对引文的还原，则主要依据是否找到引语原文和引语与原文的吻合程度来进行处理，主要分为按引文原文还原、添加注释说明作者错译现象、笔者自译等方法。

Translation is not only a process of language decoding and encoding, but also a process of cross-cultural communication. In other words, translation is not only about bilingual transformation, but also about cultural exchange. Therefore, the restoration to the original text in the process of back-translation is not only of language but also of culture. Based on the translation project of Drama Kings: Players and Publics in the Re-creation of Peking Opera, 1870-1937 written by Joshua Goldstein, this paper probes into the translation problems of Peking Opera and the strategies adopted for different cultural contents in the process of restoration.

This paper first summarizes the cultural content involved in the source text, which can be divided into two categories: vocabulary and citation. Then it analyzes the English translation of cultural content in the source text and points out the difficulties it poses to the back-translation, as they require the translator to handle carefully according to different circumstances. The paper then analyzes the problems in the Chinese translation of the second chapter of the source text, including inaccurate lexical restoration, incorrect restoration and imprecise back-translation of citations. In view of these problems, this paper puts forward appropriate translations.

For the two categories of cultural content in the source text, the present paper proposes corresponding restoration strategies. Regarding vocabulary, there are three methods, namely, doing detailed research, omitting redundant information and annotating uncommon cultural concepts. As for citations, if sources can be found, copy the source; if not, translate them on one’s own. This may involve translating in the classical style of Chinese. In this case, Baidu’s classical-Chinese machine translation, still immature but being the only one of its kind, may be applied, with the result balanced by the translator.

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　　本次翻译实践基于美国作家柯丽·斯坦珀所著的《推和敲》（Word By Word）一书。这是一本描绘词典编撰幕后工作的著作，类似的题材在国内外都属小众。笔者在试译此书的过程中发现书中表达看似简单却并不易懂，如果按照传统的直译或是意译往往无法准确传达原文语义，需采用原型转移法进行翻译。迄今为止，原型转移法在翻译研究和实践中的运用不多，学界也还未大量开展这方面的研究，这使得笔者对原型转移法产生兴趣，并以此作为研究主题。

　　在展开翻译实践前，笔者利用语料分析工具对原文语言进行分析，探讨其中的翻译难点。笔者发现，首先，作者在行文中大量地采用低频词、生僻词，不少词汇不仅没有现成对应的中文翻译，更有的难寻英文背景信息。其次，作者还会在旧词的基础上创造新义，而且不忌讳粗俗语的使用。基于此，并结合大量现有的翻译实例，笔者提出在使用原型转移法时应遵循语境原则、从主原则、等效原则，采取增减达义法、虚实互换法、巧用流行语和谐音转义法的翻译策略，以保证从语义和文化层面准确传达原文的意思，保证译文质量。

　　本研究的意义主要体现在以下三个方面：（1）引入认知科学领域的原型理论，并在此基础上进一步探讨原型转移翻译法，为翻译方法提供一个新的视角和途径，克服传统的对翻译方法非直译便意译的认知模式；（2）分析探究原型转移翻译法适用的场景，有助于打破当前仅限于品牌名、电影名等翻译方向的困境；（3）总结整理三大翻译原则和四大翻译策略，能比较有效地解决各类翻译问题。

　　总之，原型理论可为翻译提供一个认识翻译的新角度，为一些翻译议题提出新的见解，对当前的翻译实践和理论研究都具有十分积极的意义。

This paper discusses translation strategies for prototype shift in English-Chinese translations based on the translation practice of Kory Stamper’s book Word by Word. It is a nonfiction work, explicating the lexicographic details and dilemmas encountered by Stamper as an associate editor in Merriam Webster Company. During the process of analyzing the book, this paper finds that it contains many simple words with obscure meanings, which, if translated in traditional ways, for example, word for word or sense for sense, would not convey the correct meaning of the source text. For this reason, this paper proposes to adopt the prototype shift, a concept that originates from cognitive science, to solve the problem.

First of all, through literature review, this paper finds prototype shift strategy has not been widely adopted in translation practice, nor has it been extensively explored by researchers. Secondly, analysis of existing translation cases leads to three translation principles, emphasizing the linguistic context, the cultural background and the translation effects, and four translation techniques. Finally, this paper compares prototype shift strategy with some easily confused concepts, for instance, the conversion approach and the free translation.

The significance of this study is mainly reflected in the following three aspects: (1) introducing the prototype theory to provide a new perspective and approach for translation; (2) analyzing and exploring the applicable situations for prototype shift; (3) proposing three principles and four techniques to realize prototype shift effectively.

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      语言石化（Fossilization）是中介语（Interlanguage）的特征之一，是指二语学习出现停滞不前甚至倒退的现象。其中，语音、词汇、语法等层面都可能出现石化，而防止或缓解词汇石化现象中的词汇能力石化问题是本研究的核心。大量研究表明，词块（Lexical Chunk）学习能够改善词汇能力石化问题，但传统的词块教学仍有以下三方面的局限：一、无序词块的学习内容忽视了词块间的纵聚合与横组合的语义关系，学习者难以构建词义网络；二、重视记忆过程，缺少产出练习；三、一致的学习内容和方法难以满足学习者的个性化需求。这些局限性容易给学习者造成已会运用词汇的假象，进一步导致词汇能力石化问题。

       为了改善词汇能力石化问题，本文根据词汇石化、语义网络、词块教学法以及二语习得其他相关理论和自适应学习理念，结合对现有英语词汇学习工具和中国大学生词汇能力石化现状分析，设计了一款针对防止或缓解词汇能力石化问题的自适应词块学习系统，并完成了系统的原型设计。其核心思想如下：一、采用词块教学法培养学习者的词块意识和使用能力，增加词汇语境信息，避免词义直接对等，产生母语负迁移；二、通过学习概念和搭配掌握词块间的关联性，构建并激活学习者的词义网络，增加表达的多样性和准确性；三、设计不同任务复杂度的练习题型，实现对词块从识记到产出的闯关式进阶；四、设置不同学习阶段和教学反馈的自适应规则，让学习内容具有针对性并引导学习者走出词汇使用舒适区，以此来避免用词惰性，改善词汇能力石化问题。

       本研究在54名本科一年级非英语专业的学生中进行了2周的教学实验。其中10人为先导小组，以确定学习材料及实验细节；其余44人随机分为实验组和对照组各22人，前测证明两组成绩不具有显著性。实验组使用自适应词块学习系统，对照组采用传统的无序词块词表学习，两组均保证学习总量和内容完全相同。实验结束后进行后测，并在10天后进行延时测试。另外，还通过问卷调查和访谈对学习效果和满意度进行了补充验证。研究结果表明：自适应词块学习系统能够提高学习者词汇表达的多样性和准确性，并在缓解词汇能力石化上的保持效果和满意度方面都要优于传统词块学习法。

       本研究设计的自适应词块学习系统一方面能够有效缓解词汇能力石化问题，提高词汇表达的多样性和准确性；另一方面丰富了词块教学法的研究成果，对课堂教学和英语词汇学习相关工具的设计具有一定的借鉴意义。

    Fossilization is one of the main features of Interlanguage, which means that stagnancy or even backwardness occurs in the process of L2 learning. Fossilization may occur in the aspects of pronunciation, vocabulary and grammar, and the prevention and ease of vocabulary fossilization is the core issue of this study. A large number of studies have shown that the lexical approach can ease the problem of vocabulary fossilization, but the traditional lexical pedagogy still has the following three limitations: First, the disordered chunk learning materials ignore the paradigmatic and the syntagmatic relations of the semantic networks, so that learners can hardly build their semantic networks. Second, it emphasizes the memory process but lacks productive exercises. Third, unified learning materials and methods are difficult to meet the individual needs of learners. These problems can aggravate vocabulary fossilization because learners are probably unable to use appropriate and diversified words in an actual context.

       In order to make up for the above deficiencies, in light of the theories of language fossilization, semantic network, the lexical approach, other related theories of second language acquisition and the thought of adaptive learning, this paper has designed a lexical chunk learning system for the ease of vocabulary fossilization based on the analysis of the status quo of learners’ vocabulary fossilization and the existing English vocabulary learning tools. This study completed the prototype design of the system. The core ideas of the system are as follows: First, cultivating learners' lexical chunk awareness and its competence by adopting the lexical approach, so that lexical context information can be enriched and the direct equivalence of vocabulary meanings can be avoided. Second, grasping the interrelationship among lexical chunks by acquiring concepts and collocations, so that the semantic networks can be constructed and activated, and the diversity and accuracy of expressions can be enhanced. Third, realizing the process from memorization of chunks to output by setting different types of task complexity exercises. Fourth, setting adaptive rules for learning process and feedbacks, so as to improve learning pertinence and guide learners to move out of the comfort zone of using vacabulary.

    A two-week teaching experiment was conducted among 54 undergraduate freshmen of non-English majors. Among the participants, 10 were randomly chosen for pilot experiment in order to determine the learning materials and experimental details. The remaining 44 were randomly divided into the experimental group and the control group, 22 participants respectively. The former test proved that the scores of the two groups were not significant statistically. The experimental group acquired chunks by using the adaptive lexical chunk learning system; and the control group used the traditional disordered lexical chunk lists. The total amount of the learning material and the content were all the same to both groups. Post-testing was performed after the end of the experiment, and a delay test was conducted after 10 days of the post-test. In addition, the study applied questionnaires and interviews to acquire the effect and satisfaction of the different learning approach. The research results show that the adaptive lexical chunk learning system is superior to the traditional lexical learning method in terms of the ease of vocabulary fossilization, especially the diversity and accuracy of their expressions. So are the maintenance effect and satisfaction.

    The adaptive lexical chunk learning system designed in this study, on the one hand, can effectively alleviate the problem of vocabulary fossilization, especially the improvement of accuracy and diversity of expressions. On the other hand, it enriches the researches of lexical approach, and has referencing significance to classroom instruction and the design of vocabulary acquisition tools.

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  自上世纪八十年代以来，海外对中国的研究不断加强，出现了越来越多的汉学著作，中国学界也开始重视起这些著作的译介，这些译介在中国的海外汉学研究中扮演了重要角色。在海外汉学著作的翻译过程中，回译问题不可避免，由于海外汉学著作多为学术类著作，行文严谨，措辞谨慎，这也对译者的回译提出了更高的要求，需达到精准回译。本次翻译项目的源文本来自于《中国武术：从古代到21世纪》（Chinese Martial Arts: From Antiquity to the Twenty-First Century），该书介绍了中国武术的发展史，是一本典型的汉学著作，作者为美国著名历史学家龙佩（Peter Lorge）。

   本文首先阐述了本次研究的背景与意义，介绍了本次翻译的书籍以及所用到的翻译工具，然后从“海外汉学”，“回译”和“中国武术的翻译”三个角度进行了文献综述。在第三章中，笔者基于《中国武术》选译章节的翻译，总结出该书中出现的三大回译现象，即引文回译、词汇回译、以及原文错误之回译。在引文回译中，笔者将引用类型细分成“直接引用”和“间接引用”，提出不同引用类型下，引文的回译处理方式，其中对于“间接引用”的引文进行回译时，还需留意“古今异义”现象的出现；在词汇回译中，笔者将词汇分为“人名”，“武器名”和“武术相关术语”三大类，分别就这三类词汇出现的回译问题进行了探讨；在原文错误之回译中，笔者将错误分为“名称错误”和“史实描述错误”两类，就错误性质进行了定性，并对这些错误的回译方式给出了建议。

  最后笔者基于本次翻译实践的过程，提出“巧用四字格”、“合理减译”、“归化为主”以及“恪守读者视角原则”四大精准回译策略。本次研究是对武术历史类海外汉学著作精准回译策略研究的初试，以期为同类著作的翻译提供一些参考。

  During the process of translating overseas sinological works into Chinese, back translation is inevitable. Since overseas sinological works are mostly academic works written rigorously and cautiously, they set a high demand on the translator’s skills and abilities. Accurate back translation is therefore needed. The present translation project is based on Chinese Martial Arts: From Antiquity to the Twenty-First Century, authored by Peter Lorge, which discusses the history of Chinese martial arts. 

  This paper summarizes three major problems in the back translation of overseas sinological works, namely citations, special nouns, and errors in the source text. For the back translation of citations, the paper subdivides them into “direct citations” and “indirect citations”, and proposes different methods of back translation. With special nouns, this paper divides them into three categories: “names of people”, “names of weapons” and “martial-arts-related terms”, and discusses their back translation of them. As for errors in the source text, the paper divides them into two types as “errors of names” and “errors of historical description”, and advises on how to deal with them in the back translation.

  Based on this translation practice, the paper then puts forward four strategies for accurate back translation, namely “using Chinese four-character structure”, “omitting known information to the target audience”, “domesticating translation as the mainstay” and “observing the target-reader perspective”. In conclusion, this paper points out that for accurate back translation of Chinese martial arts, extensive bibliographic search is a prerequisite and careful contextualization of the object of translation is necessary.

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吉尔伯特·基思·切斯特顿是20世纪初的英国作家和记者。他生前被指控为反犹主义，如今他是否反犹仍是有争议的问题。笔者使用语料库方法研究两个问题：1､切斯特顿的犹太观点有何显著的特点？２、他的犹太观点特点是否同特定的思维模式相关？ 研究步骤如下：1､建立切斯特顿几乎全部作品语料库和同时代英国英语参考语料库，使用POS和USAS标注系统进行标注。2､收集一组犹太主题词汇，在切斯特顿语料库中研究它们的搭配，分析出切斯特顿犹太观点的特点。3､笔者认为切斯特顿在不同时期作品中广泛分布的语言特征有可能是他的思维模式的语言表征，因而笔者依据年份信息将切斯特顿语料库分组，同时也将参考语料库分组，使用cohen’s d方法计算两组语料语言特征的效应量差别，并选择cohen’s d值大于0.8的语言特征作为具有关键性的语言特征，将它们视作潜在的切斯特顿思维模式的语言表征。4、筛选出具有关键性的语言特征和搭配的重合部分，并分析它们在切斯特顿语料库中的用法，考察其用法是否与同犹太主题词汇共现时的用法相通，以此揭示切斯特顿思维模式与犹太观点的联系。 通过搭配分析，笔者发现：切斯特顿对犹太人在西方世界的存在、犹太人与金钱的关系、犹太人的人际关系都给予了负面的评价；他常常将犹太人分为不同的类型，并对这些类型进行两极化的评价；他多次将犹太教与基督教对立起来；他对犹太人的“身份”给予了一定关注。结合关键性分析，笔者发现切斯特顿的犹太观点存在背后思维模式的支撑：他将基督教作为理解其它其他宗教的参照，因而会将犹太教与基督教对立；他常常发现并展示事物的矛盾之处，而他对犹太人外在身份与实质的矛盾的关注吻合这一思维模式。

Gilbert Keith Chesterton is a British writer and journalist in the early 20th century. He is accused of Anti-Semitism when he is alive. Whether he is an Anti-Semitist is still a controversial issue today. This study uses corpus lingusitics method and tries to answer two questions: 1.What are the most prominent features of his views of Jewishness? 2. Whether those features are related to his idealogical frame of mind as a whole.

The research steps are as follows: 1. Build a corpus of most of Chesterton’s work, as well as a reference corpus of British English roughly of the same era, and tag the corpuses with POS and USAS annotation systems. 2. Collect a set of words of the Jewish theme, and calculate the collocation of lemma and semantic annotation in the Chesterton corpus. Obtain the prominent features of Chesterton’s view of Jewishness through collocation analysis. The author argues that the key linguistic features widely distributed among Chesterton’s works in different times may be the linguistic representation of his general frame of mind. Therefore, this study divides the two corpuses into two groups of texts, using Cohen's d to calculate key linguistic features. According to the benchmark, those linguistics features with Cohen’s d larger than 0.8 are selected as key features and potential representations of his general frame of mind.Then the author filters out the coincidental part of the key features and collocations, and analyzes whether their usage in the Chesterton corpus in general has relations with their usage in collocations with words of the Jewish theme, in order to reveal the connection Chesterton’s view of Jewishness and his general frame of mind.

             Through collocation analysis the author draws these conclusions: 1、Chesterton has negative opinions on the Jewish people about their existence in the Western world, their relationship with money, their interpersonal relationship with other people; 2. he often divides the Jewish into different types with only negative or positive opinions. 3. he sets up Judaism as the opposite of Christianity; 4. He is concerned about the Jewish identity. When combining those findings with key features analysis, the author finds that Chesterton's Jewish view is supported by his general frame of mind: he uses Christianity as a reference for understanding other religions, and thus pits Judaism against Christianity; he often finds and displays contradictions in things. Therefore, his concern about the contradiction between Jewish external identity and substance is consistent with this mode of thinking.

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