Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (5): 33-36.doi: 10.13190/j.jbupt.2015.05.005

• Papers • Previous Articles     Next Articles

An Entity Linking Approach Based on Context Information and Learning to Rank

TAN Yong-mei, WANG Rui, LI Mao-lin   

  1. Center for Intelligence Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2014-12-05 Online:2015-10-28 Published:2015-10-28

Abstract:

English entity linking tasks play an important role in construction of semantic network and big knowledge base. An entity linking method based on local information and learning to rank algorithm was proposed. Firstly, the context information is well used for expanding mentions' name and retrieving candidate entities from Wikipedia. Secondly, kinds of features are extracted between mentions and candidates and also the ListNet algorithm was used to rank the candidate entities to choose the most related entity as the linked objects. Finally, the NIL entities was clustered by clustering method. The method achieved 0.660 F value on KBP 2013 Entity Linking dataset, it performs 0.092 better than the median F value of all participated teams in KBP 2013 entity linking task and also performs 0.162 better than BUPTTeam 2013, which is the baseline comparison system in the experiment.

Key words: English entity linking, context information, learning to rank, ListNet algorithm, entity clustering

CLC Number: