Harnessing different knowledge sources to measure semantic relatedness under a uniform model
|Harnessing different knowledge sources to measure semantic relatedness under a uniform model|
|Author(s)||Zhang Z., Gentile A.L., Ciravegna F.|
|Published in||EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Biomedical domain, Data sets, Knowledge sources, NAtural language processing, Semantic relatedness, Uniform model, Wikipedia, Wordnet, Computational linguistics, Semantics, Natural language processing systems)|
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Harnessing different knowledge sources to measure semantic relatedness under a uniform model is a 2011 conference paper written in English by Zhang Z., Gentile A.L., Ciravegna F. and published in EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference.
Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly evaluated only in the general domain. This paper introduces a method of harnessing different knowledge sources under a uniform model for measuring semantic relatedness between words or concepts. Using Wikipedia and WordNet as examples, and evaluated in both the general and biomedical domains, it successfully combines strengths from both knowledge sources and outperforms state-of-the-art on many datasets.
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