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    On the use of automatically acquired examples for all-nouns Word Sense Disambiguation

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    Author
    Martinez, D; de Lacalle, OL; Agirre, E
    Date
    2008-01-01
    Source Title
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
    Publisher
    AI ACCESS FOUNDATION
    University of Melbourne Author/s
    Martinez, David
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Martinez, D., de Lacalle, O. L. & Agirre, E. (2008). On the use of automatically acquired examples for all-nouns Word Sense Disambiguation. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 33 (1), pp.79-107. https://doi.org/10.1613/jair.2395.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29299
    DOI
    10.1613/jair.2395
    Abstract
    <jats:p>This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.</jats:p>
    Keywords
    Artificial Intelligence and Image Processing

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