Naïve probabilistic shift-reduce parsing model using functional word based context for agglutinative languages

Yong Jae Kwak, So Young Park, Joon Ho Lim, Hae Chang Rim

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a naïve probabilistic shift-reduce parsing model which can use contextual information more flexibly than the previous probabilistic GLR parsing models, and utilize the characteristics of agglutinative language in which the functional words are highly developed. Experimental results on Korean have shown that our model using the proposed contextual information improves the parsing accuracy more effectively than the previous models. Moreover, it is compact in model size, and is robust with a small training set.

Original languageEnglish
Pages (from-to)2286-2289
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE87-D
Issue number9
Publication statusPublished - 2004 Sep

Keywords

  • Probabilistic parsing
  • Shift-reduce parsing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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