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. © 2011 ACM.
Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation / Navigli, Roberto; Faralli, Stefano; Aitor, Soroa; Oier De, Lacalle; Eneko, Agirre. - STAMPA. - (2011), pp. 2317-2320. (Intervento presentato al convegno 20th ACM Conference on Information and Knowledge Management, CIKM'11 tenutosi a Glasgow nel 24 October 2011 through 28 October 2011) [10.1145/2063576.2063955].
Two birds with one stone: Learning semantic models for text categorization and word sense disambiguation
NAVIGLI, ROBERTO;FARALLI, Stefano;
2011
Abstract
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. © 2011 ACM.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
