Two birds with one stone: learning semantic models for text categorization and word sense disambiguation
Proceedings of the 20th ACM international conference on Information and …, 2011•dl.acm.org
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 …
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 …
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.