Abstract
Entity linking refers to the task of mapping name strings in a text to their corresponding entities in a given knowledge base. It is an essential component in natural language processing applications and a challenging task. This paper proposes a method that combines heuristics and learning for entity linking by (i) learning coherence among co-occurrence entities within the text based on Wikipedia’s link structure and (ii) exploiting some heuristics based on the contexts and coreference relations among name strings. The experiment results on TAC-KBP2011 dataset show that our method achieves performance comparable to the state-of-the-art methods. The results also show that the proposed model is simple because of using a classifier trained on just two popular features in combination with some heuristics, but effective.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-05939-6_37
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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Nguyen, H.T. (2014). Combining Heuristics and Learning for Entity Linking. In: Vinh, P., Alagar, V., Vassev, E., Khare, A. (eds) Context-Aware Systems and Applications. ICCASA 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-05939-6_36
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