Pattern abstraction and term similarity for Word Sense Disambiguation: IRST at Senseval-3

Pattern Abstraction and Term Similarity for Word Sense Disambiguation: IRST at Senseval-3 Carlo Strapparava and Alfio Gliozzo and Claudio Giuliano ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, I-38050 Trento, ITALY {strappa, gliozzo, giuliano}@itc.it Abstract This paper summarizes IRST’s participation in Senseval-3. We participated both in the English all- words task and in some lexical sample tasks (En- glish, Basque, Catalan, Italian, Spanish). We fol- lowed two perspectives. On one hand, for the all- words task, we tried to refine the Domain Driven Disambiguation that we presented at Senseval-2. The refinements consist of both exploiting a new technique (Domain Relevance Estimation) for do- main detection in texts, and experimenting with the use of Latent Semantic Analysis to avoid reliance on manually annotated domain resources (e.g. WORD- NET DOMAINS). On the other hand, for the lexical sample tasks, we explored the direction of pattern abstraction and we demonstrated the feasibility of leveraging external knowledge using kernel meth- ods. 1 Introduction The starting point for our research in the Word Sense Disambiguation (WSD) area was to explore the use of semantic domains in order to solve lex- ical ambiguity. At the Senseval-2 competition we proposed a new approach to WSD, namely Domain Driven Disambiguation (DDD). This approach con- sists of comparing the estimated domain of the con- text of the word to be disambiguated with the do- mains of its senses, exploiting the property of do- mains to be features of both texts and words. The domains of the word senses can be either inferred from the learning data or derived from the informa- tion in WORDNET DOMAINS. For Senseval-3, we refined the DDD methodol- ogy with a fully unsupervised technique - Domain Relevance Estimation (DRE) - for domain detection in texts. DRE is performed by an expectation maxi- mization algorithm for the gaussian mixture model, which is exploited to differentiate relevant domain information in texts from noise. This refined DDD system was presented in the English all-words task. Originally DDD was developed to assess the use- fulness of domain information for WSD. Thus it did not exploit other knowledge sources commonly used for disambiguation (e.g. syntactic patterns or collocations). As a consequence the performance of the DDD system is quite good for precision (it dis- ambiguates well the “domain” words), but as far as recall is concerned it is not competitive compared with other state of the art techniques. On the other hand DDD outperforms the state of the art for unsu- pervised systems, demonstrating the usefulness of domain information for WSD. In addition, the DDD approach requires domain annotations for word senses (for the experiments we used WORDNET DOMAINS, a lexical resource de- veloped at IRST). Like all manual annotations, such an operation is costly (more than two man years have been spent for labeling the whole WORDNET DOMAINS structure) and affected by subjectivity. Thus, one drawback of the DDD methodology was a lack of portability among languages and among different sense repositories (unless we have synset- aligned WordNets). Besides …

Content truncated to 500 words