Automatic acronym dictionary construction based on acronym generation types

Yeo Chan Yoon, So Young Park, Young In Song, Hae Chang Rim, Dae Woong Rhee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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 acronymdefinition 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.

Original languageEnglish
Pages (from-to)1584-1587
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number5
Publication statusPublished - 2008 May


  • Acronym
  • Automatic dictionary construction

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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