Robust WSD Task

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Abstract

This paper presents the participation of the semantic N-levels search engine SENSE at the CLEF 2009 Ad Hoc Robust-WSD Task. Our aim is to demonstrate that the combination of the N-levels model and WSD can improve the retrieval performance even when an effective retrieval model is adopted. To reach this aim, we worked on two different strategies. On one hand a model, based on Okapi BM25, was adopted at each level. On the other hand, we integrated a local relevance feedback technique, called Local Context Analysis, in both indexing levels of the system (keyword and word meaning). The hypothesis that Local Context Analysis can be effective even when it works on word meanings coming from a WSD algorithm is supported by experimental results. In monolingual task MAP increased of about 2% exploiting disambiguation, while CMAP increased from 4% to 9% when we used WSD in both mono- and bi- lingual tasks.

Autore Pugliese

Tutti gli autori

  • CAPUTO A.;SEMERARO G.;BASILE P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2010

ISSN

0302-9743

ISBN

978-3-642-15753-0

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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