A note on morphological analysis methods based on statistical decision theory

Yasunari Maeda, Naoya Ikeda, Hideki Yoshida, Yoshitaka Fujiwara, Toshiyasu Matsushima

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

Abstract

Morphological analysis is one of important topics in the field of NLP(Natural Language Processing). In many previous research a HMM(Hidden Markov Model) with unknown parameters has been used as a language model. In this research we also use the HMM as the language model. And we assume that sate transitions in the HMM are dominated by a second order Markov chain. At first we propose two types of morphological analysis methods which minimize the error rate with reference to a Bayes criterion. But the computational complexity of the proposed Bayes optimal morphological analysis methods are exponential order. So we also propose approximate methods.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages1563-1568
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

Keywords

  • Hidden markov model
  • Morphological analysis
  • Statistical decision theory

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Maeda, Y., Ikeda, N., Yoshida, H., Fujiwara, Y., & Matsushima, T. (2007). A note on morphological analysis methods based on statistical decision theory. In SICE Annual Conference, SICE 2007 (pp. 1563-1568). [4421232] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421232