Abstract
In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. In addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster. © 2009 Association for Computational Linguistics.
Cite
CITATION STYLE
Agirre, E., & Soroa, A. (2009). Personalizing PageRank for word sense disambiguation. In EACL 2009 - 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings (pp. 33–41). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1609067.1609070
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