Combining POS tagging, Lucene search and similarity metrics for entity linking

Shujuan Zhao, Chune Li, Shuai Ma, Tiejun Ma, Dianfu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Entity linking is to detect proper nouns or concrete concepts (a.k.a mentions) from documents, and to map them to the corresponding entries in a given knowledge base. In this paper, we propose an entity linking framework POSLS consisting of three components: mention detection, candidate selection and entity disambiguation. First, we use part of speech tagging and English syntactic rules to detect mentions. We then choose candidates with Lucene search. Finally, we identify the best matchings with a similarity based disambiguation method. Experimental results show that our approach has an acceptable accuracy.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings
PublisherSpringer
Pages503-509
Number of pages7
EditionPART 1
ISBN (Electronic)978-3-642-41230-1
ISBN (Print)978-3-642-41229-5
DOIs
Publication statusPublished - 13 Oct 2013
Event14th International Conference on Web Information Systems Engineering - Nanjing, China
Duration: 13 Oct 201315 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8180 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Web Information Systems Engineering
Abbreviated titleWISE 2013
CountryChina
CityNanjing
Period13/10/1315/10/13

Keywords

  • Entity Linking
  • Lucene Search
  • Mention Detection
  • POS Tagging
  • Similarity Metrics

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