Combining POS tagging, lucene search and similarity metrics for entity linking
Combining POS tagging, lucene search and similarity metrics for entity linking
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.
entity linking, pos tagging, lucene search, similarity metrics, mention detection
978-3-642-41229-5
503-509
Zhao, Shujuan
57419c07-b419-4f03-af06-6828e53db834
Li, Chune
9922e361-dbe6-47a3-8502-ff749152b375
Ma, Shuai
232b6412-5735-42cd-9cbc-80352bff65ce
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Ma, Dianfu
96986a0e-15d4-4b81-8482-59b8ed03863d
2013
Zhao, Shujuan
57419c07-b419-4f03-af06-6828e53db834
Li, Chune
9922e361-dbe6-47a3-8502-ff749152b375
Ma, Shuai
232b6412-5735-42cd-9cbc-80352bff65ce
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Ma, Dianfu
96986a0e-15d4-4b81-8482-59b8ed03863d
Zhao, Shujuan, Li, Chune, Ma, Shuai, Ma, Tiejun and Ma, Dianfu
(2013)
Combining POS tagging, lucene search and similarity metrics for entity linking.
In Web Information Systems Engineering – WISE 2013.
Springer.
.
(doi:10.1007/978-3-642-41230-1_44).
Record type:
Conference or Workshop Item
(Paper)
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.
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More information
Published date: 2013
Venue - Dates:
14th International Conference Web Information Systems Engineering (WISE), Nanjing, China, 2013-10-13 - 2013-10-15
Keywords:
entity linking, pos tagging, lucene search, similarity metrics, mention detection
Organisations:
Southampton Business School
Identifiers
Local EPrints ID: 366855
URI: http://eprints.soton.ac.uk/id/eprint/366855
ISBN: 978-3-642-41229-5
PURE UUID: 725dc259-c0d2-49fe-9bc7-9bb17d9dd1a4
Catalogue record
Date deposited: 15 Jul 2014 11:28
Last modified: 14 Mar 2024 17:18
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Contributors
Author:
Shujuan Zhao
Author:
Chune Li
Author:
Shuai Ma
Author:
Dianfu Ma
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