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.

该研究的主要目的是开发实用的语义分析系统SAGE。为此，我们试图根据原始原型来提高精确性和速度的性能。在精确度方面，我们做出了四项努力来改善它。（1）除了从EDR语料库中计算出的统计措施以确定单词之间的深层情况外，我们还基于基于粒子，语音部分和单词的信息来确定两个单词之间的情况，以确定两个单词之间的情况含义。（2）我们竭尽全力确定不可分割的单词及其修饰符之间的深层案例。（3）可以通过用具有相似概念的注册单词代替EDR字典中未注册单词的深层案例。（4）我们将包含括号的表达式分为三类，补充，佣金和补充，并将括号的分析添加到鼠尾草中。当前使用上述技术的系统在单词含义上达到了90.2％的精度，在90.0％上，90.0％的精度在90.0％上的精度为90.0％。来自EDR语料库的句子的深度案例和87.0％和86.8％的新闻文章分别为Internet。在加速方面，我们设计了一个线性订单算法，包括两个决定单词含义和深层案例的步骤。我们首先看到修改后的单词如何影响修饰符的含义，然后以概率表示。之后，我们使用自上而下的方法确定每个子句的含义。我们通过算法实施了大约10万次的速度。此外，我们将访问EDR字典所需的时间减少到了原始时间的第五个，并将整个系统从序言版本转换为C版本，因此实现了另外5次的速度。