The University of Alicante systems at Senseval-3

The University of Alicante systems at SENSEVAL-3∗ Sonia V´azquez, Rafael Romero Armando Su´arez and Andr´es Montoyo Dpt. of Software and Computing Systems Universidad de Alicante, Spain {svazquez,romero}@dlsi.ua.es {armando,montoyo}@dlsi.ua.es Iulia Nica and Antonia Mart´ı † Dpt. of General Linguistics Universidad de Barcelona, Spain iulia@clic.fil.ub.es amarti@ub.edu Abstract The DLSI-UA team is currently working on sev- eral word sense disambiguation approaches, both supervised and unsupervised. These approaches are based on different ways to use both annotated and unannotated data, and several resources generated from or exploiting WordNet (Miller et al., 1993), WordNet Domains, EuroWordNet (EWN) and addi- tional corpora. This paper presents a view of differ- ent system results for Word Sense Disambiguation in different tasks of SENSEVAL-3. 1 Introduction Word Sense Disambiguation (WSD) is an open re- search field in Natural Language Processing (NLP). The task of WSD consists in assigning the correct sense to words in a particular context using an elec- tronic dictionary as the source of words definitions. This is a difficult problem that is receiving a great deal of attention from the research community. At the Second International Workshop on Evaluating Word Sense Disambiguation Systems, SENSEVAL-2, several supervised and unsupervised systems took part. The more successful systems re- lay on corpus-based and supervised learning meth- ods. At SENSEVAL-2 the average level of accu- racy achieved rounded 70%, which is insufficient for such other NLP tasks as information retrieval, machine translation, or question answering. The DLSI-UA systems were applied to three SENSEVAL-3 tasks: English all-words, English lex- ical sample and Spanish Lexical Sample. Our sys- tems use both corpus-based and knowledge-based approaches: Maximum Entropy(ME) (Lau et al., 1993; Berger et al., 1996; Ratnaparkhi, 1998) is a corpus-based and supervised method based on linguistic features; ME is the core of a bootstrap- ping algorithm that we call re-training inspired ∗ This paper has been partially supported by the Spanish Government (CICyT) under project number TIC-2003-7180 and the Valencia Government (OCyT) under project number CTIDIB-2002-151 by co-training (Blum and Mitchell, 1998); Rele- vant Domains (RD) (Montoyo et al., 2003) is a resource built from WordNet Domains (Magnini and Cavaglia, 2000) that is used in an unsuper- vised method that assigns domain and sense la- bels; Specification Marks(SP) (Montoyo and Palo- mar, 2000) exploits the relations between synsets stored in WordNet (Miller et al., 1993) and does not need any training corpora; Commutative Test (CT) (Nica et al., 2003), based on the Sense Discrimi- nators device derived from EWN (Vossen, 1998), disambiguates nouns inside their syntactic patterns, with the help of information extracted from raw cor- pus. A resume of which methods and how were used in which SENSEVAL-3 tasks is shown in Table 1. DLSI-UA Method Combined Systems Results ALL-NOSU RD No LS-ENG-SU Re-t No LS-ENG- NOSU RD No LS-SPA-SU ME+Re-t No LS-SPA-NOSU SM + ME Nouns: SM Verbs and adj.: ME LS-SPA- Pattern-Nica Nouns: SM PATTERN + ME Verbs and adj.: ME Table 1: DLSI-UA Systems at SENSEVAL-3 Most of these methods are relatively new and our goal when participating …

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