A distributional semantics approach to simultaneous recognition of multiple classes of named entities

Siddhartha Jonnalagadda; Robert Leaman; Trevor Cohen; Graciela Gonzalez

doi:10.1007/978-3-642-12116-6_19

A distributional semantics approach to simultaneous recognition of multiple classes of named entities

Siddhartha Jonnalagadda^*, Robert Leaman, Trevor Cohen, Graciela Gonzalez

^*Corresponding author for this work

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

8 Scopus citations

Abstract

Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing tools apply features and rules mainly at the word level or use lexicons. Recent advances in distributional semantics allow us to efficiently create paradigmatic models that encode word order. We used Sahlgren et al's permutation-based variant of the Random Indexing model to create a scalable and efficient system to simultaneously recognize multiple entity classes mentioned in natural language, which is validated on the GENIA corpus which has annotations for 46 biomedical entity classes and supports nested entities. Using distributional semantics features only, it achieves an overall micro-averaged Fmeasure of 67.3% based on fragment matching with performance ranging from 7.4% for "DNA substructure" to 80.7% for "Bioentity".

Original language	English (US)
Title of host publication	Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings
Pages	224-235
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-12116-6_19
State	Published - 2010
Externally published	Yes
Event	11th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2010 - Iasi, Romania Duration: Mar 21 2010 → Mar 27 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6008 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2010
Country/Territory	Romania
City	Iasi
Period	3/21/10 → 3/27/10

Keywords

Biomedical
Classification
Distributional
Entity
GENIA
Multiple
Named
Recognition
Semantics

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-12116-6_19

Cite this

Jonnalagadda, S., Leaman, R., Cohen, T., & Gonzalez, G. (2010). A distributional semantics approach to simultaneous recognition of multiple classes of named entities. In Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings (pp. 224-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6008 LNCS). https://doi.org/10.1007/978-3-642-12116-6_19

Jonnalagadda, Siddhartha ; Leaman, Robert ; Cohen, Trevor et al. / A distributional semantics approach to simultaneous recognition of multiple classes of named entities. Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings. 2010. pp. 224-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A distributional semantics approach to simultaneous recognition of multiple classes of named entities",

abstract = "Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing tools apply features and rules mainly at the word level or use lexicons. Recent advances in distributional semantics allow us to efficiently create paradigmatic models that encode word order. We used Sahlgren et al's permutation-based variant of the Random Indexing model to create a scalable and efficient system to simultaneously recognize multiple entity classes mentioned in natural language, which is validated on the GENIA corpus which has annotations for 46 biomedical entity classes and supports nested entities. Using distributional semantics features only, it achieves an overall micro-averaged Fmeasure of 67.3% based on fragment matching with performance ranging from 7.4% for {"}DNA substructure{"} to 80.7% for {"}Bioentity{"}.",

keywords = "Biomedical, Classification, Distributional, Entity, GENIA, Multiple, Named, Recognition, Semantics",

author = "Siddhartha Jonnalagadda and Robert Leaman and Trevor Cohen and Graciela Gonzalez",

note = "Funding Information: This work was financially supported by the Chinese National Basic Research Program (2015CB150404) and the Bohai sea granary engineering saline and alkaline land use program (75030).; 11th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2010 ; Conference date: 21-03-2010 Through 27-03-2010",

year = "2010",

doi = "10.1007/978-3-642-12116-6_19",

language = "English (US)",

isbn = "3642121152",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "224--235",

booktitle = "Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings",

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Jonnalagadda, S, Leaman, R, Cohen, T & Gonzalez, G 2010, A distributional semantics approach to simultaneous recognition of multiple classes of named entities. in Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6008 LNCS, pp. 224-235, 11th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2010, Iasi, Romania, 3/21/10. https://doi.org/10.1007/978-3-642-12116-6_19

A distributional semantics approach to simultaneous recognition of multiple classes of named entities. / Jonnalagadda, Siddhartha; Leaman, Robert; Cohen, Trevor et al.
Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings. 2010. p. 224-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6008 LNCS).

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

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AU - Jonnalagadda, Siddhartha

AU - Leaman, Robert

AU - Cohen, Trevor

AU - Gonzalez, Graciela

N1 - Funding Information: This work was financially supported by the Chinese National Basic Research Program (2015CB150404) and the Bohai sea granary engineering saline and alkaline land use program (75030).

PY - 2010

Y1 - 2010

N2 - Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing tools apply features and rules mainly at the word level or use lexicons. Recent advances in distributional semantics allow us to efficiently create paradigmatic models that encode word order. We used Sahlgren et al's permutation-based variant of the Random Indexing model to create a scalable and efficient system to simultaneously recognize multiple entity classes mentioned in natural language, which is validated on the GENIA corpus which has annotations for 46 biomedical entity classes and supports nested entities. Using distributional semantics features only, it achieves an overall micro-averaged Fmeasure of 67.3% based on fragment matching with performance ranging from 7.4% for "DNA substructure" to 80.7% for "Bioentity".

AB - Named Entity Recognition and Classification is being studied for last two decades. Since semantic features take huge amount of training time and are slow in inference, the existing tools apply features and rules mainly at the word level or use lexicons. Recent advances in distributional semantics allow us to efficiently create paradigmatic models that encode word order. We used Sahlgren et al's permutation-based variant of the Random Indexing model to create a scalable and efficient system to simultaneously recognize multiple entity classes mentioned in natural language, which is validated on the GENIA corpus which has annotations for 46 biomedical entity classes and supports nested entities. Using distributional semantics features only, it achieves an overall micro-averaged Fmeasure of 67.3% based on fragment matching with performance ranging from 7.4% for "DNA substructure" to 80.7% for "Bioentity".

KW - Biomedical

KW - Classification

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KW - Recognition

KW - Semantics

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Y2 - 21 March 2010 through 27 March 2010

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Jonnalagadda S, Leaman R, Cohen T, Gonzalez G. A distributional semantics approach to simultaneous recognition of multiple classes of named entities. In Computational Linguistics and Intelligent Text Processing - 11th International Conference, CICLing 2010, Proceedings. 2010. p. 224-235. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-12116-6_19

A distributional semantics approach to simultaneous recognition of multiple classes of named entities

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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