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Study on Integration of Statistical Information and Linguistic Constraint Information

统计信息与语言约束信息的整合研究

基本信息

批准号：
12480089
负责人：
MATSUMOTO Yuji
金额：
$ 6.21万
依托单位：
NARA INSTITUTE OF SCIENCE AND TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-12480089/
关键词：
Head-driven Phrase Structure Grammar Constraint-based Grammar Formalism Dependency Analysis Morphological Analysis Statistical Natural Language Processing Machine Learning Support Vector Machines Integration of Statistical and Constraint Inf

项目摘要

Along with the increase of machine readable linguistic data, statistical natural language processing has been actively researched. However, most of the statistical natural language processing aims at surface language processing, and is not appropriate to detailed semaintical language analysis. On the other hand, constraint-base grammar formalisms such as Head-driven Phrase Structure Grammar attempt to describe linguistic phenomena as lexical knowledge and most of the linguistic constraints are presented in the lexicon. While such a grammar formalism specifies complicated linguistic information in a very modular way, they have a drawback that any input that violate linguistic constraints cannot be parsed in any way.This research aimed at compensating drawback of both approaches by integrating both mechanisms : We first implemented a rubust and high-quality word-based dependency analysis of sentences using statistical information. Then the constraint-based grammar formalism receiving the output of statistical dependency information, finds out possible interpretation according to the dependency structure. To achieve a robust language processing, we implemented a constraint relaxing mechanism. We implemented the idea of type coersion and co-composition proposed in Generative Lexicon as well as an user interface to browse the intermediate processing information.As for dependency analysis, we utilized Support Vector Machines so as to cope with a large scale feature space, and devised a deterministic bottom-up parsing algorithm for Japanese and English. We implemented a part of Japanese grammar based on Head-driven Phrase Structure Grammar. Those statistical and constraint-based grammar and parser are runnable in the user-inteface we developed to be used for the grammar developpers and the users of the natural language processing system.

沿着机器可读语言数据的增加，统计自然语言处理得到了积极的研究。然而，大多数统计自然语言处理的目标是表面的语言处理，并不适合详细的语义语言分析。另一方面，基于约束的语法形式主义，如中心语驱动短语结构语法，试图将语言现象描述为词汇知识，大多数语言约束都在词汇中呈现。虽然这样的语法形式主义规定了复杂的语言信息在一个非常模块化的方式，他们有一个缺点，任何违反语言约束的输入不能以任何方式解析。本研究旨在弥补这两种方法的缺点，通过整合两种机制：我们首先实现了一个rubust和高质量的基于单词的依赖分析的句子使用统计信息。然后，基于约束的语法形式化接收统计依赖信息的输出，根据依赖结构找出可能的解释。为了实现一个强大的语言处理，我们实现了一个约束放松机制。我们实现了Generative Lexicon中提出的类型强制和协同组合的思想，并提供了一个用户界面来浏览中间处理信息;在依赖分析方面，我们利用支持向量机来科普大规模的特征空间，并设计了一个确定性的自底向上的日语和英语句法分析算法。我们实现了一部分基于中心语驱动短语结构语法的日语语法。这些基于统计和约束的语法和语法分析器可以在我们开发的用户界面中运行，供语法开发人员和自然语言处理系统的用户使用。