Study on Information Retrieval based on Similarity Calculation of Intra-Document Structure

本研究的目的是建立一种基于“内容”的信息检索方法。本研究将“内容”看作是以下几项的组合：a）用标签标注的文档的逻辑结构，B）文本，c）信息抽取技术抽取的信息。基于段落间相似性的文档内结构提取：通过不仅利用文档内信息，还利用文档间信息，提高了文档相关部分的检索效率.基于机器学习和抽取模式的多策略命名实体识别器：通过将这两类策略结合起来进行命名实体任务，提高了命名实体识别的准确率.数字信息的提取及其在问句检索中的应用：我们认为问句检索是一种理想的上下文检索系统。命名实体对应于4W类型问题的答案。另一方面，与H型问题相对应的是数值表达式。因此，我们提出了一种方法来提取数值表达式及其上下文作为QA系统的一部分。

The purpose of this research is establishment of the method for "content"-based information retrieval. In our research, the "content" is regarded as the combination of the following items : a) Logical structure of document annotated by tags, b) Text, and c) Information extracted by the technology of Information Extraction.Through the two year research, we obtained the following results :1. Extraction of structure of intra-documents based on similarity among passages :By using not only intra-document information but also inter-document information, we improve the effectiveness of retrieving relevant portions of document.2. Multi-strategic named entity recognizer based on machine learning and extraction patterns :By combining those two types of strategies for named entity task, we improve the accuracy of recognition of named entities.3. Extraction of numerical information and its application to Question Answering :We consider "Question Answering" is the one of the ideal context retrieval system. Named entities correspond to the answer for the 4W-type questions. On the other hand, it is numerical expressions what corresponds to H-type questions. Therefore, we proposed a method to extract numerical expressions with its context as a part of a QA system.