Why general Word Sense Disambiguation (WSD) systems are not (to the best of our knowledge) being used in real-world applications? In our opinion, some plausible reasons are: • General Word Sense Disambiguation, as an intermediate task, is frequently harder than the final application. This is probably the case of the most commonly mentioned WSD applications: mono and multilingual Information Ret...

Word Sense Disambiguation (WSD) is considered as one of the toughest problem in the field of Natural Language Processing. IndoWordNet is a linked structure of WordNets of major Indian languages. Recently, several IndoWordNet based WSD approaches have been proposed and implemented for Indian languages. In this chapter, we present the usage of various other features of IndoWordNet in performing W...

Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses. We addressed the MFS bias in WSD systems by combining the output from a WSD system with a set of mostly static features to create a MFS classifier to decide when to and not to c...

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of...

Although it is generally agreed that Word Sense Disambiguation (WSD) is an application dependent task, the great majority of the efforts has aimed at the development of WSD systems without considering their application. We argue that this strategy is not appropriate, since some aspects, such as the sense repository and the disambiguation process itself, vary according to the application. Taking...

This paper describes the University of Sydney’s WSD and Lexical Substitution systems for SemEval-2007. These systems are principally based on evaluating the substitutability of potential synonyms in the context of the target word. Substitutability is measured using Pointwise Mutual Information as obtained from the Web1T corpus. The WSD systems are supervised, while the Lexical Substitution syst...

There is a renewed interest in word sense disambiguation (WSD) as it contributes to various applications in natural language processing. Applications for which WSD is potentially an issue are: Machine Translation, Information Retrieval (IR), QA systems, Dialogue systems,etc. In this paper we survey vector-based methods for WSD in machine learning approache.

In this paper, we first argue that various ways of using context in WSD can be considered as distinct representations of a polysemous word under consideration, then all these representations are used jointly to identify the meaning of the target word. Under such a consideration, we can then straightforwardly apply the general framework for combining classifiers developed in Kittler et al. [5] t...

Word sense disambiguation (WSD) and coreference resolution are two fundamental tasks for natural language processing. Unfortunately, they are seldom studied together. In this paper, we propose to incorporate the coreference resolution technique into a word sense disambiguation system for improving disambiguation precision. Our work is based on the existing instance knowledge network (IKN) based...

Although it has been always thought that Word Sense Disambiguation (WSD) can be useful for Machine Translation, only recently efforts have been made towards integrating both tasks to prove that this assumption is valid, particularly for Statistical Machine Translation (SMT). While different approaches have been proposed and results started to converge in a positive way, it is not clear yet how ...