Paper Info
Title
A knowledge graph based solution for entity discovery and linking in open-domain questions
Abstract
Named entity discovery and linking is the fundamental and core component of question answering. In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement. To overcome these difficulties, we proposed a knowledge graph based solution for QEDL and developed a system consists of Question Entity Discovery (QED) module and Entity Linking (EL) module. The method of QED module is a tradeoff and ensemble of two methods. One is the method based on knowledge graph retrieval, which could extract more entities in questions and guarantee the recall rate, the other is the method based on Conditional Random Field (CRF), which improves the precision rate. The EL module is treated as a ranking problem and Learning to Rank (LTR) method with features such as semantic similarity, text similarity and entity popularity is utilized to extract and make full use of the information in short texts. On the official dataset of a shared QEDL evaluation task, our approach could obtain 64.44% F1 score of QED and 64.86% accuracy of EL, which ranks the 2nd place and indicates its practical use for QEDL problem. © Springer International Publishing AG 2018.
Year
DOI
Venue
2018
10.1007/978-3-319-73830-7_19
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DocType
Volume
Citations 
Journal
10699 LNCS
0
PageRank 
References 
Authors
0.34
12
6
Name
Order
Citations
PageRank
Lei Kai115738.17
Zhang Bing201.35
Zhang Bing301.35
Liu Yong401.35
Yang Deng5113.78
Shen Ying6102.54