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)

    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 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
    VolumeE91-D
    Issue number5
    DOIs
    Publication statusPublished - 2008 May

    Keywords

    • 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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