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Semantic and Contextual Analysis using Common knowledge from Japanese Articles

使用日语文章中的常识进行语义和上下文分析

基本信息

批准号：
13680461
负责人：
HARADA Minoru
金额：
$ 1.41万
依托单位：
AOYAMA GAKUIN UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680461/
关键词：
semantic analysis word meaning deep case parse tree analysis of complex sentence automatic summarization correspondence analysis EDR ゼロ代名詞従属節連体節

项目摘要

The main aim of the research is to develop a practical semantic analysis system SAGE. For this aim, we attempted to improve the performance both in precision and speed based on the original prototype.In regard to the precision, we made four efforts to improve it. (1) In addition to the statistical measures calculated from the EDR corpus to determine the deep case among words, we supplement a rule-based procedure to determine the case between two words, based on the information on particles, parts of speech, and word meanings. (2) We contrived to determine the deep case between an indeclinable word and its modifier. (3) It has been made possible to analyze the deep case of unregistered words in the EDR Dictionary by replacing them with the registered words holding similar concepts. (4) We classified the expressions containing brackets into 3 categories, supplementation, commission, and complement, and add the analysis of brackets into SAGE.The present system employing the above techniques has reached a precision of 90.2% on word meaning, 90.0% on deep case for the sentences from EDR Corpus, and 87.0% and 86.8% respectively for news articles from the Internet.In regard to the speed-up, we devised a linear-order algorithm including two steps for deciding word meaning and deep case. We first see how the modified word impacts the meaning of the modifier from bottom up, and then represent it with a probability. After that, we determine the meaning of each clause with a top-down approach. We implemented a speed-up of about 10 thousand times with the algorithm. Besides, we reduced the time required for accessing the EDR Dictionary to 1 fifth of the original time, and converted the whole system from a Prolog version to a C version and hence realized another speed-up of 5 times.

本课题的主要目的是开发一个实用的语义分析系统SAGE。为此，我们试图在原有原型的基础上提高精度和速度。在精度方面，我们做了四个方面的努力来提高它。（1）除了从EDR语料库计算的统计措施来确定词之间的深度大小写之外，我们还补充了一个基于规则的过程来确定两个词之间的大小写，基于粒子、词性、字的含义。(2)我们设法确定了一个不可辨别的词和它的修饰词之间的深格。(3)通过用具有类似概念的注册词替换未注册词，可以分析EDR词典中未注册词的深层情况。(4)本文将含有括号的句子分为补充、委托和补充三类，并将括号分析加入到SAGE中，对EDR语料库中的句子进行了语义分析和深格分析，其准确率分别达到90.2%和90.0%。对于互联网上的新闻文章，正确率分别为87.0%和86.8%.在加速方面，我们设计了一种线性排序算法，包括确定词义和深度格两个步骤.我们首先从下往上看修饰词如何影响修饰词的含义，然后用概率表示它。然后，我们用自上而下的方法确定每个子句的含义。我们实现了约1万倍的速度与算法。此外，我们还将EDR字典的访问时间缩短到原来的1/5，并将整个系统从Prolog版本转换为C版本，实现了5倍的速度提升。