Recognizing Transliteration Equivalence for Enriching Domain-Specific Thesauri

被引:0
|
作者
Oh, Jong-Hoon [1 ]
Choi, Key-Sun [1 ]
机构
[1] Natl Inst Informat & Commun Technol, Computat Linguist Grp, Informat & Network Syst Dept, Seika, Kyoto 6190289, Japan
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Transliteration is used to translate proper names and technical terms especially from languages in Roman alphabets to languages in non-Roman alphabets such as from English to Korean, Japanese, and Chinese. "Transliteration equivalence" refers to a set of the same words that include all possible transliterated forms and the original word. Many Korean domain-specific terms are composed of transliterations. Therefore, handling transliterations and their transliteration equivalence is essential to constructing and enriching Korean domain-specific thesauri. In this paper, we propose an algorithm recognizing transliteration equivalence or transliteration pairs in domain-specific dictionaries using machine transliteration. Machine transliteration can serve as one of components in a transliteration pair acquisition method by offering a machine-generated transliterated form. Because, transliteration pair acquisition task is to find phonetic cognate in two languages, it is important to phonetically convert words in one language to that in the other language, like machine transliteration, to compare the phonetic equivalence. Our method shows about 99% precision and 73% recall rate.
引用
收藏
页码:231 / 237
页数:7
相关论文
共 50 条
  • [1] Mining domain-specific Thesauri from Wikipedia: A case study
    Milne, David
    Medelyan, Olena
    Witten, H.
    2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), 2006, : 442 - +
  • [2] Enriching Linked Data with Semantics from Domain-Specific Diagrammatic Models
    Buchmann, Robert A.
    Karagiannis, Dimitris
    BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2016, 58 (05): : 341 - 353
  • [3] Enriching Linked Data with Semantics from Domain-Specific Diagrammatic Models
    Robert A. Buchmann
    Dimitris Karagiannis
    Business & Information Systems Engineering, 2016, 58 : 341 - 353
  • [4] Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
    Hoffman, Matthew D.
    Johnson, Matthew J.
    Tran, Dustin
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [5] PROOF OF EQUIVALENCE OF SEMANTIC METHODS FOR A SELECTED DOMAIN-SPECIFIC LANGUAGE
    Steingartner, William
    Novitzka, Valerie
    Schreiner, Wolfgang
    JOURNAL OF APPLIED MATHEMATICS AND COMPUTATIONAL MECHANICS, 2024, 23 (02) : 79 - 92
  • [6] General and domain-specific techniques for detecting and recognizing superimposed text in video
    Zhang, DQ
    Rajendran, RK
    Chang, SF
    2002 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL I, PROCEEDINGS, 2002, : 593 - 596
  • [7] Enriching Domain-Specific Language Models Using Domain Independent WWW N-Gram Corpus
    Chang, Harry
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II, 2012, 7268 : 38 - 46
  • [8] Domain-specific model differencing for graphical domain-specific languages
    Jafarlou, Manouchehr Zadahmad
    ACM/IEEE 25TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS, MODELS 2022 COMPANION, 2022, : 205 - 208
  • [9] An Active Learning Approach to Recognizing Domain-Specific Queries From Query Log
    Ni, Weijian
    Liu, Tong
    Sun, Haohao
    Wei, Zhensheng
    WEB AND BIG DATA, APWEB-WAIM 2017, PT II, 2017, 10367 : 18 - 32
  • [10] DSMCompare: domain-specific model differencing for graphical domain-specific languages
    Manouchehr Zadahmad
    Eugene Syriani
    Omar Alam
    Esther Guerra
    Juan de Lara
    Software and Systems Modeling, 2022, 21 : 2067 - 2096