Automatic Acronym Dictionary Construction Based on Acronym Generation Types
Summary :
In this paper, we propose a new model of automatically constructing an acronym dictionary. The proposed model generates possible acronym candidates from a definition, and then verifies each acronym-definition pair with a Naive Bayes classifier based on web documents. In order to achieve high dictionary quality, the proposed model utilizes the characteristics of acronym generation types: a syllable-based generation type, a word-based generation type, and a mixed generation type. Compared with a previous model recognizing an acronym-definition pair in a document, the proposed model verifying a pair in web documents improves approximately 50% recall on obtaining acronym-definition pairs from 314 Korean definitions. Also, the proposed model improves 7.25% F-measure on verifying acronym-definition candidate pairs by utilizing specialized classifiers with the characteristics of acronym generation types.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.5 pp.1584-1587
- Publication Date
- 2008/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.5.1584
- Type of Manuscript
- LETTER
- Category
- Natural Language Processing
Authors
Keyword
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Yeo-Chan YOON, So-Young PARK, Young-In SONG, Hae-Chang RIM, Dae-Woong RHEE, "Automatic Acronym Dictionary Construction Based on Acronym Generation Types" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 5, pp. 1584-1587, May 2008, doi: 10.1093/ietisy/e91-d.5.1584.
Abstract: In this paper, we propose a new model of automatically constructing an acronym dictionary. The proposed model generates possible acronym candidates from a definition, and then verifies each acronym-definition pair with a Naive Bayes classifier based on web documents. In order to achieve high dictionary quality, the proposed model utilizes the characteristics of acronym generation types: a syllable-based generation type, a word-based generation type, and a mixed generation type. Compared with a previous model recognizing an acronym-definition pair in a document, the proposed model verifying a pair in web documents improves approximately 50% recall on obtaining acronym-definition pairs from 314 Korean definitions. Also, the proposed model improves 7.25% F-measure on verifying acronym-definition candidate pairs by utilizing specialized classifiers with the characteristics of acronym generation types.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.5.1584/_p
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@ARTICLE{e91-d_5_1584,
author={Yeo-Chan YOON, So-Young PARK, Young-In SONG, Hae-Chang RIM, Dae-Woong RHEE, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Acronym Dictionary Construction Based on Acronym Generation Types},
year={2008},
volume={E91-D},
number={5},
pages={1584-1587},
abstract={In this paper, we propose a new model of automatically constructing an acronym dictionary. The proposed model generates possible acronym candidates from a definition, and then verifies each acronym-definition pair with a Naive Bayes classifier based on web documents. In order to achieve high dictionary quality, the proposed model utilizes the characteristics of acronym generation types: a syllable-based generation type, a word-based generation type, and a mixed generation type. Compared with a previous model recognizing an acronym-definition pair in a document, the proposed model verifying a pair in web documents improves approximately 50% recall on obtaining acronym-definition pairs from 314 Korean definitions. Also, the proposed model improves 7.25% F-measure on verifying acronym-definition candidate pairs by utilizing specialized classifiers with the characteristics of acronym generation types.},
keywords={},
doi={10.1093/ietisy/e91-d.5.1584},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Automatic Acronym Dictionary Construction Based on Acronym Generation Types
T2 - IEICE TRANSACTIONS on Information
SP - 1584
EP - 1587
AU - Yeo-Chan YOON
AU - So-Young PARK
AU - Young-In SONG
AU - Hae-Chang RIM
AU - Dae-Woong RHEE
PY - 2008
DO - 10.1093/ietisy/e91-d.5.1584
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2008
AB - In this paper, we propose a new model of automatically constructing an acronym dictionary. The proposed model generates possible acronym candidates from a definition, and then verifies each acronym-definition pair with a Naive Bayes classifier based on web documents. In order to achieve high dictionary quality, the proposed model utilizes the characteristics of acronym generation types: a syllable-based generation type, a word-based generation type, and a mixed generation type. Compared with a previous model recognizing an acronym-definition pair in a document, the proposed model verifying a pair in web documents improves approximately 50% recall on obtaining acronym-definition pairs from 314 Korean definitions. Also, the proposed model improves 7.25% F-measure on verifying acronym-definition candidate pairs by utilizing specialized classifiers with the characteristics of acronym generation types.
ER -
English
