基于汉语话题的句际关系自动分析研究

话题和无标记的主次复句是语言类型学上汉语的两个显赫范畴，话题识别和句际关系分析有着重要的应用价值。本项目论证了话题是复杂的句际结构生成的形式标记，将利用话题来实现意合性小句的计算机自动理解。(1)汉语话题自动识别，话题小句识别将在全新视角下对逗号进行分类，话题成分识别将基于大规模语料库语义泛化后进行语义相似度计算来验证候选话题句的合格性。(2)句际层级结构分析，利用话题、关联标记、句间相似度自顶向下对文本进行分隔。(3)句际逻辑关系判定，利用小句的事件义、情感义、否定义、时态义、结构义等不同语义范畴特征进行SVM分类，将利用LDA模型将词汇特征转换为主题空间中的三类特征，将利用LinkLDA模型来捕捉逻辑关系对两个论元的协同语义制约。(4)验证系统，基于大规模语料来拟合不同句际关系的权值来提升文本情感计算的性能。（5）语言资源建设，将构建100万字、15万句的汉语话题和句际关系标注语料库。

Topic and unmarked complex sentence are two kinds of mighty categories within the framework of linguistic typology, and topic identification and discourse relation recognition have wide and important application usages in natural language processing. This project proposes that topic is the marked form to generate Chinese complex discourse structure, and then exploits topic information for the computer automatic understanding of Chinese parataxis subsentence. (1) Topic identification: Topic subsentence identification will classify Chinese common from a new perspective, and topic element identification will verify the fitness of candidate topic sentences by computing semantic similarity after semantic generalization in a large-scale corpus. (2) Discourse structure analysis: It will segment complex sentences by using topic, connective and similarity score. (3) Discourse relation recognition: It will employ a SVM classifier using different semantic category features including Event, Polarity, Negation, Modality and Structure. It will use LDA model to transform lexical features into topic space with three types of features, and it will use LinkLDA to jointly model the two arguments of a semantic relation. (4) A test system: It will automatically train the weights of different relations in accordance with their importance to improve the performance of text sentiment analysis. (5) Language knowledge base: We will construct a large-scale high quality corpus annotated with Chinese topic and discourse relations.