Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation
|Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation|
|Author(s)||Navigli R., Faralli S., Soroa A., De Lacalle O., Agirre E.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||text classification, word sense disambiguation (Extra: Learning semantics, Multiple domains, Random Walk, Semantic Model, Text categorization, Text classification, Wikipedia, Word Sense Disambiguation, Wordnet, Knowledge management, Natural language processing systems, Semantics, Text processing)|
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Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation is a 2011 conference paper written in English by Navigli R., Faralli S., Soroa A., De Lacalle O., Agirre E. and published in International Conference on Information and Knowledge Management, Proceedings.
In this paper we present a novel approach to learning semantic models for multiple domains, which we use to categorize Wikipedia pages and to perform domain Word Sense Disambiguation (WSD). In order to learn a semantic model for each domain we first extract relevant terms from the texts in the domain and then use these terms to initialize a random walk over the WordNet graph. Given an input text, we check the semantic models, choose the appropriate domain for that text and use the best-matching model to perform WSD. Our results show considerable improvements on text categorization and domain WSD tasks.
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