IS DESCRIBING LANGUAGE MERE BUTTERFLY COLLECTION? ON EPISTEMOLOGY, STATISTICAL LANGUAGE MODELS, AND CORPUS

被引:0
|
作者
de Uzeda-Garrao, Milena [1 ]
机构
[1] Univ Fed Rural Rio de Janeiro, Seropedica, RJ, Brazil
关键词
Language Philosophy; Statistical Models; Corpus Linguistics;
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This work focuses on the philosophical tension between conventionalists and rationalists, present in the history of science from pre-Socratic period to contemporary Linguistics. R also takes into account the relevance of statistical models rather than considering language phenomenon as an elegant representation of the expression of thought. For that purpose, we take some conventionalists' accounts of language throughout philosophy history and we also analyze computer scientist Peter Norvig's recent essay on whether language science should really take the path linguist Noam Chomsky considers as the only one to a scientific approach. Therefore, in this work, we attempt to take empirical facts and, therefore, pure description, to portray linguistic phenomenon as an evidence of a conventional use rather than a rational creative human impetus. Finally, we also claim that Corpus Linguistics grounds Norvig's arguments on language science.
引用
收藏
页码:10900 / 10903
页数:4
相关论文
共 50 条
  • [21] Anbar: Collection and analysis of a large scale Urdu language Twitter corpus
    Tahir, Bilal
    Mehmood, Muhammad Amir
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 42 (05) : 4789 - 4800
  • [22] The Norwegian Colossal Corpus: A Text Corpus for Training Large Norwegian Language Models
    Kummervold, Per E.
    Wetjen, Freddy
    de la Rosa, Javier
    LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2022, : 3852 - 3860
  • [23] Incorporating linguistic structure into statistical language models
    Rosenfeld, R
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2000, 358 (1769): : 1311 - 1324
  • [24] Statistical Knowledge Assessment for Large Language Models
    Dong, Qingxiu
    Xu, Jingjing
    Kong, Lingpeng
    Sui, Zhifang
    Li, Lei
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [25] Statistical language models for intelligent XML retrieval
    Hiemstra, D
    INTELLIGENT SEARCH ON XML DATA: APPLICATIONS, LANGUAGES, MODELS IMPLEMENTATIONS AND BENCHMARKS, 2003, 2818 : 107 - 118
  • [26] An extended clustering algorithm for statistical language models
    Ueberla, JP
    IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 1996, 4 (04): : 313 - 316
  • [27] Statistical language models for topographic data recognition
    Winstanley, A
    Salaik, B
    Keyes, L
    IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 1808 - 1810
  • [28] Generalized algorithms for constructing statistical language models
    Allauzen, C
    Mohri, M
    Roark, B
    41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, 2003, : 40 - 47
  • [29] Fertility models for statistical natural language understanding
    Della Pietra, S
    Epstein, M
    Roukos, S
    Ward, T
    35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, 1997, : 168 - 173
  • [30] Predicting reading difficulty with statistical language models
    Collins-Thompson, K
    Callan, J
    JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2005, 56 (13): : 1448 - 1462