Evaluating knowledge-poor and knowledge-rich features in automatic classification: A case study in WSD

Author

Zampieri, Marcos

Author_Institution

Univ. of Cologne, Cologne, Germany

fYear

2012

fDate

20-22 Nov. 2012

Firstpage

359

Lastpage

363

Abstract

Word Sense Disambiguation (WSD) is a fundamental task in many Computational Linguistics applications. It consists of automatically identifying the sense of ambiguous words in context using computational methods. This work evaluates the automatic disambiguation performance of five machine learning classifiers: Naive Bayes, Support Vector Machines, Decision Trees, KStar and Maximum Entropy. For the classification we compare the performance of these algorithms using knowledge-rich and knowledge-poor features applied to Portuguese data.

Keywords

Bayes methods; computational linguistics; decision trees; learning (artificial intelligence); pattern classification; support vector machines; KStar; Portuguese data; WSD; ambiguous words; automatic classification; automatic disambiguation performance; computational linguistic applications; decision trees; knowledge-poor feature evaluation; knowledge-rich feature evaluation; machine learning classifiers; maximum entropy classifier; naive Bayes classifier; support vector machines; word sense disambiguation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Informatics (CINTI), 2012 IEEE 13th International Symposium on

Conference_Location

Budapest

Print_ISBN

978-1-4673-5205-5

Electronic_ISBN

978-1-4673-5210-9

Type

conf

DOI

10.1109/CINTI.2012.6496790

Filename

6496790