Abstract
Entity Linking (EL) is a fundamental technology in Natural Language Processing and Knowledge Engineering. Previous works mainly focus on linking mentioned names recognized in news or articles to knowledge base. However, in social network, user-generated content is quite different from typical news text. Users sometimes use words more informally, even create new words. One entity may have different aliases mentioned by web users, so identifying these aliases calls for more attention than before. Several methods are proposed to mine aliases and a learning-to-rank framework is applied to combine different types of feature together. A binary classifier based on SVM is trained to judge whether the top one candidate given by ranking algorithm is accepted. The evaluation results of NLP&CC 2013 Entity Linking Track shows the effectiveness of this framework.
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Wang, Y., Luo, C., Li, X., Liu, Y., Zhang, M., Ma, S. (2013). Entity Linking from Microblogs to Knowledge Base Using ListNet Algorithm. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_26
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DOI: https://doi.org/10.1007/978-3-642-41644-6_26
Publisher Name: Springer, Berlin, Heidelberg
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