Abstract
One of the most successful approaches to Word Sense Disambiguation (WSD) in the last decade has been the knowledge-based approach, which exploits lexical knowledge sources such as Wordnets, ontologies, etc. The knowledge encoded in them is typically used as a sense inventory and as a relations bank. However, this type of information is rather sparse in terms of senses and the relations among them. In this paper we present a strategy for the enrichment of WSD knowledge bases with data-driven relations from a gold standard corpus (annotated with word senses, syntactic analyses, etc.). We focus on English as use case, but our approach is scalable to other languages. The results show that the addition of new knowledge improves the accuracy of WSD task.
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Notes
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In the knowledge graph constructed in this way and distributed with the UKB system, the relation between the noun synset and the verb synset for have is not presented.
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This result for English is far from state-of-the-art, but it is based only on 25 % of SemCor. Also, our goal here is only to compare the various knowledge graphs.
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Acknowledgements
This research has received partial support by the EC’s FP7 project: “QTLeap: Quality Translation by Deep Language Engineering Approaches” (610516).
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Simov, K., Osenova, P., Popov, A. (2016). Using Context Information for Knowledge-Based Word Sense Disambiguation. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_13
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DOI: https://doi.org/10.1007/978-3-319-44748-3_13
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