Using WordNet to disambiguate word senses for text classification

Ying Liu; Peter Scheuermann; Xingsen Li; Xingquan Zhu

doi:10.1007/978-3-540-72588-6_127

Using WordNet to disambiguate word senses for text classification

Ying Liu^*, Peter Scheuermann, Xingsen Li, Xingquan Zhu

^*Corresponding author for this work

Electrical and Computer Engineering

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

37 Scopus citations

Abstract

In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.

Original language	English (US)
Title of host publication	Computational Science - ICCS 2007 - 7th International Conference, Proceedings
Publisher	Springer Verlag
Pages	781-789
Number of pages	9
Edition	PART 3
ISBN (Print)	9783540725879
DOIs	https://doi.org/10.1007/978-3-540-72588-6_127
State	Published - 2007
Event	7th International Conference on Computational Science, ICCS 2007 - Beijing, China Duration: May 27 2007 → May 30 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	4489 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Conference on Computational Science, ICCS 2007
Country/Territory	China
City	Beijing
Period	5/27/07 → 5/30/07

Keywords

Disambiguation
Text classification
Word sense
WordNet

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-72588-6_127

Cite this

Liu, Y., Scheuermann, P., Li, X., & Zhu, X. (2007). Using WordNet to disambiguate word senses for text classification. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings (PART 3 ed., pp. 781-789). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-540-72588-6_127

Liu, Ying ; Scheuermann, Peter ; Li, Xingsen et al. / Using WordNet to disambiguate word senses for text classification. Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. Springer Verlag, 2007. pp. 781-789 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{6988c7251efb440c8a907324d44eaa79,

title = "Using WordNet to disambiguate word senses for text classification",

abstract = "In this paper, we propose an automatic text classification method based on word sense disambiguation. We use {"}hood{"} algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.",

keywords = "Disambiguation, Text classification, Word sense, WordNet",

author = "Ying Liu and Peter Scheuermann and Xingsen Li and Xingquan Zhu",

year = "2007",

doi = "10.1007/978-3-540-72588-6_127",

language = "English (US)",

isbn = "9783540725879",

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

publisher = "Springer Verlag",

number = "PART 3",

pages = "781--789",

booktitle = "Computational Science - ICCS 2007 - 7th International Conference, Proceedings",

edition = "PART 3",

note = "7th International Conference on Computational Science, ICCS 2007 ; Conference date: 27-05-2007 Through 30-05-2007",

}

Liu, Y, Scheuermann, P, Li, X & Zhu, X 2007, Using WordNet to disambiguate word senses for text classification. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4489 LNCS, Springer Verlag, pp. 781-789, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 5/27/07. https://doi.org/10.1007/978-3-540-72588-6_127

Using WordNet to disambiguate word senses for text classification. / Liu, Ying; Scheuermann, Peter; Li, Xingsen et al.

Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. Springer Verlag, 2007. p. 781-789 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

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

TY - GEN

T1 - Using WordNet to disambiguate word senses for text classification

AU - Liu, Ying

AU - Scheuermann, Peter

AU - Li, Xingsen

AU - Zhu, Xingquan

PY - 2007

Y1 - 2007

N2 - In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.

AB - In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.

KW - Disambiguation

KW - Text classification

KW - Word sense

KW - WordNet

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DO - 10.1007/978-3-540-72588-6_127

M3 - Conference contribution

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SN - 9783540725879

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

SP - 781

EP - 789

BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings

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T2 - 7th International Conference on Computational Science, ICCS 2007

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Liu Y, Scheuermann P, Li X, Zhu X. Using WordNet to disambiguate word senses for text classification. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 ed. Springer Verlag. 2007. p. 781-789. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-540-72588-6_127

Using WordNet to disambiguate word senses for text classification

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Keywords

ASJC Scopus subject areas

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