Learning classifier system approach to natural language grammar induction

被引：0

作者：

Unold, Olgierd ^{[1
]}

机构：

[1] Wroclaw Univ Technol, Inst Comp Engn Control & Robot, PL-50370 Wroclaw, Poland

来源：

COMPUTATIONAL SCIENCE - ICCS 2007, PT 2, PROCEEDINGS | 2007年 / 4488卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes an evolutionary approach to the problem of inferring non-stochastic context-free grammar (CFG) from natural language (NL) corpora. The approach employs Grammar-based Classifier System (GCS). GCS is a new version of Learning Classifier Systems in which classifiers are represented by CFG in Chomsky Normal Form. GCS has been tested on the NL corpora, and it provided comparable results to the pure genetic induction approach, but in a significantly shorter time. The efficient implementation for grammar induction is very important during analysis of large text corpora.

引用

页码：1210 / 1213

页数：4

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