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

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

doi:10.1007/978-3-642-41230-1_44

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

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

^*Corresponding author for this work

School of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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 language	English
Title of host publication	Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings
Pages	503-509
Number of pages	7
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-41230-1_44
State	Published - 2013
Event	14th International Conference on Web Information Systems Engineering, WISE 2013 - Nanjing, China Duration: 13 Oct 2013 → 15 Oct 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8180 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Web Information Systems Engineering, WISE 2013
Country/Territory	China
City	Nanjing
Period	13/10/13 → 15/10/13

Keywords

Entity Linking
Lucene Search
Mention Detection
POS Tagging
Similarity Metrics

Access to Document

10.1007/978-3-642-41230-1_44

Cite this

Zhao, S., Li, C., Ma, S., Ma, T., & Ma, D. (2013). Combining POS tagging, Lucene search and similarity metrics for entity linking. In Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings (PART 1 ed., pp. 503-509). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8180 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-41230-1_44

Zhao, Shujuan ; Li, Chune ; Ma, Shuai et al. / Combining POS tagging, Lucene search and similarity metrics for entity linking. Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings. PART 1. ed. 2013. pp. 503-509 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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

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.",

keywords = "Entity Linking, Lucene Search, Mention Detection, POS Tagging, Similarity Metrics",

author = "Shujuan Zhao and Chune Li and Shuai Ma and Tiejun Ma and Dianfu Ma",

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note = "14th International Conference on Web Information Systems Engineering, WISE 2013 ; Conference date: 13-10-2013 Through 15-10-2013",

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Zhao, S, Li, C, Ma, S, Ma, T & Ma, D 2013, Combining POS tagging, Lucene search and similarity metrics for entity linking. in Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8180 LNCS, pp. 503-509, 14th International Conference on Web Information Systems Engineering, WISE 2013, Nanjing, China, 13/10/13. https://doi.org/10.1007/978-3-642-41230-1_44

Combining POS tagging, Lucene search and similarity metrics for entity linking. / Zhao, Shujuan; Li, Chune; Ma, Shuai et al.
Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings. PART 1. ed. 2013. p. 503-509 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8180 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combining POS tagging, Lucene search and similarity metrics for entity linking

AU - Zhao, Shujuan

AU - Li, Chune

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AU - Ma, Tiejun

AU - Ma, Dianfu

PY - 2013

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N2 - 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.

AB - 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.

KW - Entity Linking

KW - Lucene Search

KW - Mention Detection

KW - POS Tagging

KW - Similarity Metrics

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DO - 10.1007/978-3-642-41230-1_44

M3 - 会议稿件

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SN - 9783642412295

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings

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Y2 - 13 October 2013 through 15 October 2013

ER -

Zhao S, Li C, Ma S, Ma T, Ma D. Combining POS tagging, Lucene search and similarity metrics for entity linking. In Web Information Systems Engineering, WISE 2013 - 14th International Conference, Proceedings. PART 1 ed. 2013. p. 503-509. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-41230-1_44

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

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