Co-Occurrence-Based Error Correction Approach to Word Segmentation

View Sample PDF

Author(s): Ekawat Chaowicharat (Mahidol University, Thailand)and Kanlaya Naruedomkul (Mahidol University, Thailand)
Copyright: 2012
Pages: 11
Source title: Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches
Source Author(s)/Editor(s): Chutima Boonthum-Denecke (Hampton University, USA), Philip M. McCarthy (The University of Memphis, USA)and Travis Lamkin (University of Memphis, USA)
DOI: 10.4018/978-1-61350-447-5.ch023

Keywords: Artificial Intelligence / Computational Linguistics / Computer Science & IT / Information Science Reference

Purchase

View Co-Occurrence-Based Error Correction Approach to Word Segmentation on the publisher's website for pricing and purchasing information.

Abstract

A number of word segmentation algorithms have been offered in the past; however, there is still room for improvement. Co-occurrence-Based Error Correction (CBEC), the proposed approach in this chapter, is a novel Thai word segmentation approach that was designed to provide accurate segmentation results based on context and purpose. CBEC quickly segments the input string using any available algorithm; maximal matching was used in the experiment. Next, CBEC checks its segmentation output against an error risk data bank to determine if there is any error risk. The error risk data bank is developed based on a training corpus. The current version of the error risk bank was based on the training corpus available at BEST 2009. Then, CBEC re-segments the input string using the co-occurrence score of the word sequence to ensure the accuracy of the segmentation result.

The IRMA Community

Research IRM

Co-Occurrence-Based Error Correction Approach to Word Segmentation

Purchase

Abstract

Related Content

IRMA Sponsors