Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

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Word sense disambiguation: A survey

This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.

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Using Wikipedia for Automatic Word Sense Disambiguation

In word sense disambiguation, a system attempts to determine the sense of a word from contextual features. Major barriers to building a high-performing word sense disambiguation system include the difficulty of labeling data for this task and of predicting fine-grained sense distinctions. These issues stem partly from the fact that the task is being treated in isolation from possible uses of automatically disambiguated data. In this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. We can use parallel language corpora as a large supply of partially labeled data for this task. We present algorithms for solving the word translation problem and demonstrate a significant improvement over a baseline system. We then show that the word-translation system can be used to improve performance on a simplified machine-translation task and can effectively and accurately prune the set of candidate translations for a word.

/pdf/word-sense-disambiguation-for-machine-translation-5dhxe45a1l.pdf

Word-Sense Disambiguation for Machine Translation

Systems and methods for word sense disambiguation, including discerning one or more senses or occurrences, distinguishing between senses or occurrences, and determining a meaning for a sense or occurrence of a subject term. In a collection of documents containing terms and a reference collection containing at least one meaning associated with a term, the method includes forming a vector space representation of terms and documents. In some embodiments, the vector space is a latent semantic index vector space. In some embodiments, occurrences are clustered to discern or distinguish a sense of a term. In preferred embodiments, meaning of a sense or occurrence is assigned based on either correlation with an external reference source, or proximity to a reference source that has been indexed into the space.

Word sense disambiguation

Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural Language Processing (NLP), i.e., those of, respectively, automatically assigning meaning to words in context from a predefined sense inventory and discovering senses from text for a given input word. The two tasks have generally been hard to perform with high accuracy. However, today innovations in approach to WSD and WSI are promising to open up many interesting new horizons in NLP and Information Retrieval applications. This paper is a quick tour on how to start doing research in this exciting field and suggests the hottest topics to focus on.

/pdf/a-quick-tour-of-word-sense-disambiguation-induction-and-1ea8q5ea8h.pdf

A quick tour of word sense disambiguation, induction and related approaches

A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. Our investigation reveals that this method of acquiring sense-tagged data is promising. On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs.

Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study

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Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study