Abstract
This paper presents our approach for NLPCC 2015 shared task, Entity Recognition and Linking in Chinese Search Queries. The proposed approach takes a query as input, and generates a ranked mentionentity links as results. It combines several different metrics to evaluate the probability of each entity link, including entity relatedness in the given knowledge graph, document similarity between query and the virtual document of entity in the knowledge graph. In the evaluation, our approach gets 33.2% precision and 65.2% recall, and ranks the 6th among all the 14 teams according to the average F1-measure.
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CITATION STYLE
Li, J., Pan, J., Ye, C., Huang, Y., Wen, D., & Wang, Z. (2015). Linking entities in chinese queries to knowledge graph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9362, pp. 590–597). Springer Verlag. https://doi.org/10.1007/978-3-319-25207-0_56
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