Research Article
Combining Heuristics and Learning for Entity Linking
@INPROCEEDINGS{10.1007/978-3-319-05939-6_36, author={Hien Nguyen}, title={Combining Heuristics and Learning for Entity Linking}, proceedings={Context-Aware Systems and Applications. Second International Conference, ICCASA 2013, Phu Quoc Island, Vietnam, November 25-26, 2013, Revised Selected Papers}, proceedings_a={ICCASA}, year={2014}, month={6}, keywords={Entity linking Entity disambiguation Wikification}, doi={10.1007/978-3-319-05939-6_36} }- Hien Nguyen
Year: 2014
Combining Heuristics and Learning for Entity Linking
ICCASA
Springer
DOI: 10.1007/978-3-319-05939-6_36
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.
