Word sense disambiguation in information retrieval revisited

Stokoe, Christopher; Oakes, Michael; Tait, John

doi:10.1145/860465.860466

Cited by 54 publications

(61 citation statements)

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“…However, previous investigations into the role of WSD in IR have shown that low accuracy in WSD negated any possible performance increase from ambiguity resolution [1,2]. This suggests that improving the performance of WSD systems is crucial for applications to attain benefits from WSD.…”

Section: Introductionmentioning

confidence: 99%

Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

Chen

Palmer

2005

Lecture Notes in Computer Science

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Abstract. This paper shows that our WSD system using rich linguistic features achieved high accuracy in the classification of English SENSEVAL2 verbs for both fine-grained (64.6%) and coarse-grained (73.7%) senses. We describe three specific enhancements to our treatment of rich linguistic features and present their separate and combined contributions to our system's performance. Further experiments showed that our system had robust performance on test data without high quality rich features.

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Section: Introductionmentioning

confidence: 99%

Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

Chen

Palmer

2005

Lecture Notes in Computer Science

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“…Current approaches for managing semantic variation mainly work at word level, using WordNet (Miller et al, 1990) as their knowledge source (Arampatzis et al, 2000). Such techniques are very sensitive to word-sense ambiguity, requiring the employment of high-performance word-sense disambiguation techniques (Stevenson, 2003;Stokoe et al, 2003). Even so, real improvements are usually obtained only for incomplete and relatively short queries (Voorhees, 1994).…”

Section: Discussionmentioning

confidence: 99%

Extraction of complex index terms in non-English IR: A shallow parsing based approach

Vilares

Alonso

Vilares

2008

Information Processing & Management

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The performance of Information Retrieval systems is limited by the linguistic variation present in natural language texts. Word-level Natural Language Processing techniques have been shown to be useful in reducing this variation. In this article, we summarize our work on the extension of these techniques for dealing with phrase-level variation in European languages, taking Spanish as a case in point. We propose the use of syntactic dependencies as complex index terms in an attempt to solve the problems deriving from both syntactic and morpho-syntactic variation and, in this way, to obtain more precise index terms. Such dependencies are obtained through a shallow parser based on cascades of finite-state transducers in order to reduce as far as possible the overhead due to this parsing process. The use of different sources of syntactic information, queries or documents, has been also studied, as has the restriction of the dependencies applied to those obtained from noun phrases. Our approaches have been tested using the CLEF corpus, obtaining consistent improvements with regard to classical word-level non-linguistic techniques. Results show, on the one hand, that syntactic information extracted from documents is more useful than that from queries. On the other hand, it has been demonstrated that by restricting dependencies to those corresponding to noun phrases, important reductions of storage and management costs can be achieved, albeit at the expense of a slight reduction in performance.

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“…He also indicated that improvements in IR performance would be observed only if WSD could be performed with at least 90% accuracy. Encouraging evidence of the usefulness of WSD in IR has come from [30]. Assuming a WSD accuracy greater than 90%, they showed that the use of WSD in IR improves the precision by about 4.3%.…”

Section: Information Retrieval (Ir)mentioning

confidence: 99%

Automatic Concept Extraction in Semantic Summarization Process

Carbonaro¹

2012

Advances in Knowledge Representation

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Word sense disambiguation in information retrieval revisited

Cited by 54 publications

References 0 publications

Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features

Extraction of complex index terms in non-English IR: A shallow parsing based approach

Automatic Concept Extraction in Semantic Summarization Process

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