It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Zhi Zhong, Hwee Tou Ng
• Keywords: ACL 2010, Computational Linguistics, Open Source Wsd, Supervised English All-words, Word Sense Disambiguation