Enriching Context Information for Entity Linking with Web Data

Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relatio...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computer science and technology 2020-07, Vol.35 (4), p.724-738
Main Authors: Wang, Yi-Ting, Shen, Jie, Li, Zhi-Xu, Yang, Qiang, Liu, An, Zhao, Peng-Peng, Xu, Jia-Jie, Zhao, Lei, Yang, Xun-Jie
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Computational linguistics
Computer Science
Context
Data Structures and Information Theory
Database searching
Information Systems Applications (incl.Internet)
Internet/Web search services
Knowledge bases (artificial intelligence)
Language processing
Methods
Natural language interfaces
Online searching
Regular Paper
Search engines
Software Engineering
Theory of Computation
Towns
Web services
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F 1 score of entity linking.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-020-0280-1