Dependency syntax analysis using grammar induction and a lexical categories precedence system

Hiram Calvo, Omar J. Gambino, Alexander Gelbukh, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The unsupervised approach for syntactic analysis tries to discover the structure of the text using only raw text. In this paper we explore this approach using Grammar Inference Algorithms. Despite of still having room for improvement, our approach tries to minimize the effect of the current limitations of some grammar inductors by adding morphological information before the grammar induction process, and a novel system for converting a shallow parse to dependencies, which reconstructs information about inductor's undiscovered heads by means of a lexical categories precedence system. The performance of our parser, which needs no syntactic tagged resources or rules, trained with a small corpus, is 10% below to that of commercial semi-supervised dependency analyzers for Spanish, and comparable to the state of the art for English.

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings
Pages109-120
Number of pages12
EditionPART 1
DOIs
Publication statusPublished - 2011 Mar 9
Externally publishedYes
Event12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011 - Tokyo, Japan
Duration: 2011 Feb 202011 Feb 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6608 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011
CountryJapan
CityTokyo
Period11/2/2011/2/26

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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