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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Computational linguistics - Association for Computational Linguistics, 2019-09, Vol.45 (3), p.559-601 [Tạp chí có phản biện]

Copyright MIT Press Journals, The Sep 2019 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0891-2017 ;EISSN: 1530-9312 ;DOI: 10.1162/coli_a_00357

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  • Nhan đề:
    Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
  • Tác giả: Ponti, Edoardo Maria ; O’Horan, Helen ; Berzak, Yevgeni ; Vulić, Ivan ; Reichart, Roi ; Poibeau, Thierry ; Shutova, Ekaterina ; Korhonen, Anna
  • Chủ đề: Algorithms ; Cognitive science ; Computer Science ; Document and Text Processing ; Humanities and Social Sciences ; Language typology ; Language universals ; Language variation ; Linguistics ; Literature reviews ; Machine learning ; Methods and statistics ; Natural language processing ; Semantics
  • Là 1 phần của: Computational linguistics - Association for Computational Linguistics, 2019-09, Vol.45 (3), p.559-601
  • Mô tả: Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.
  • Nơi xuất bản: One Rogers Street, Cambridge, MA 02142-1209, USA: MIT Press
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 0891-2017
    EISSN: 1530-9312
    DOI: 10.1162/coli_a_00357
  • Nguồn: HAL SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)
    Hyper Article en Ligne (HAL) (Open Access)
    Alma/SFX Local Collection
    DOAJ Directory of Open Access Journals
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