An Empirical Study of Classifier Combination Based Word Sense Disambiguation

Wenpeng LU  Hao WU  Ping JIAN  Yonggang HUANG  Heyan HUANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.1   pp.225-233
Publication Date: 2018/01/01
Publicized: 2017/08/23
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7090
Type of Manuscript: PAPER
Category: Natural Language Processing
word sense disambiguation,  classifier combination,  probability weighted voting method,  self-adaptation,  

Full Text: PDF>>
Buy this Article

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.