Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.

Computational Approach to Anaphora Resolution in Spanish Dialogues

This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora.