Polyphonic Word Disambiguation with Machine Learning Approaches

Five different classification models, namely RFR_SUM, CRFs, Maximum Entropy, SVM and Semantic Similarity Model, are employed for polyphonic disambiguation. Based on observation of the experiment outcome of these models, an additional ensemble method based on majority voting is proposed. The ensemble...

Full description

Saved in:
Bibliographic Details
Published in2010 Fourth International Conference on Genetic and Evolutionary Computing pp. 244 - 247
Main Authors Jinke Liu, Weiguang Qu, Xuri Tang, Yizhe Zhang, Yuxia Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Context
Context modeling
CRFs
Ensemble model
Entropy
Kernel
Machine learning
Maximum Entropy
Polyphone disambiguation
RFR_SUM
Semantic Similarity
Semantics
Support vector machines
SVM
Online AccessGet full text

Cover

More Information
Summary:Five different classification models, namely RFR_SUM, CRFs, Maximum Entropy, SVM and Semantic Similarity Model, are employed for polyphonic disambiguation. Based on observation of the experiment outcome of these models, an additional ensemble method based on majority voting is proposed. The ensemble method obtains an average precision of 96.78%, which is much better than the results obtained in previous literatures.
ISBN:9781424488919
1424488915
DOI:10.1109/ICGEC.2010.67