Fine-Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets

Fine-Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets Dan TUFIŞ Institute for Artificial Intelligence 13, “13 Septembrie” Bucharest, 050711 Romania tufis@racai.ro Radu ION Institute for Artificial Intelligence 13, “13 Septembrie” Bucharest, 050711 Romania radu@racai.ro Nancy IDE Department of Computer Science Vassar College Poughkeepsie, NY 12604-0520 USA ide@cs.vassar.edu Abstract The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. The wordnets are aligned to the Princeton Wordnet, according to the principles established by EuroWordNet. The evaluation of the WSD system, implementing the method described herein showed very encouraging results. The same system used in a validation mode, can be used to check and spot alignment errors in multilingually aligned wordnets as BalkaNet and EuroWordNet. 1 Introduction Word Sense Disambiguation (WSD) is well- known as one of the more difficult problems in the field of natural language processing, as noted in (Gale et al, 1992; Kilgarriff, 1997; Ide and Véronis, 1998), and others. The difficulties stem from several sources, including the lack of means to formalize the properties of context that characterize the use of an ambiguous word in a given sense, lack of a standard (and possibly exhaustive) sense inventory, and the subjectivity of the human evaluation of such algorithms. To address the last problem, (Gale et al, 1992) argue for upper and lower bounds of precision when comparing automatically assigned sense labels with those assigned by human judges. The lower bound should not drop below the baseline usage of the algorithm (in which every word that is disambiguated is assigned the most frequent sense) whereas the upper bound should not be too restrictive” when the word in question is hard to disambiguate even for human judges (a measure of this difficulty is the computation of the agreement rates between human annotators). Identification and formalization of the determining contextual parameters for a word used in a given sense is the focus of WSD work that treats texts in a monolingual setting—that is, a setting where translations of the texts in other languages either do not exist or are not considered. This focus is based on the assumption that for a given word w and two of its contexts C1 and C2, if C1 ≡ C2 (are perfectly equivalent), then w is used with the same sense in C1 and C2. A formalized definition of context for a given sense would then enable a WSD system to accurately assign sense labels to occurrences of w in unseen texts. Attempts to characterize context for a given sense of a word have addressed a variety of factors: • Context length: what is the size of the window of text that should be considered to determine context? Should it consist of only a few words, or include much larger portions of text? • Context …

Content truncated to 500 words