Graph-ranking collective Chinese entity linking algorithm  

作　　者：Tao XIE Bin WU Bingjing JIA Bai WANG 

机构地区：[1]Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Frontiers of Computer Science》2020年第2期291-303,共13页中国计算机科学前沿（英文版）

基　　金：the National Basic Research(973)Program of China(2013CB329606);the Natural Science Research Program of Anhui Science and Technology University(ZRC2016494).

摘　　要：Entity linking(EL)systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base.Existing EL approaches usually ignore the semantic correlation between the mentions in the text,and are limited to the scale of the local knowledge base.In this paper,we propose a novel graphranking collective Chinese entity linking(GRCCEL)algorithm,which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources.By improved weighted word2vec textual similarity and improved PageRank algorithm,more semantic information and structural information can be captured in the document.With an incremental evidence mining process,more powerful discrimination capability for similar entities can be obtained.We evaluate the performance of our algorithm on some open domain corpus.Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

关 键 词：COLLECTIVE ENTITY LINKING knowledge mapping word embedding ENTITY correlation graph PAGERANK 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]