Verb similarity: Comparing corpus and psycholinguistic data

被引:0
|
作者
Gil-Vallejo, Lara [1 ]
Coll-Florit, Marta [1 ]
Castellon, Irene [2 ]
Turmo, Jordi [3 ]
机构
[1] Univ Oberta Catalunya, Dept Arts & Humanities, Barcelona, Catalunya, Spain
[2] Univ Barcelona, Dept Linguist, Barcelona, Catalunya, Spain
[3] Univ Politecn Cataluna, Dept Comp Sci, Barcelona, Catalunya, Spain
关键词
similarity; semantic roles; word associations; SEMANTIC SIMILARITY; WORD-ASSOCIATIONS; KNOWLEDGE; REPRESENTATIONS; ACTIVATION; INDUCTION; SELECTION; SPANISH; RECALL; ROLES;
D O I
10.1515/cllt-2016-0045
中图分类号
H0 [语言学];
学科分类号
030303 ; 0501 ; 050102 ;
摘要
Similarity, which plays a key role in fields like cognitive science, psycholinguistics and natural language processing, is a broad and multifaceted concept. In this work we analyse how two approaches that belong to different perspectives, the corpus view and the psycholinguistic view, articulate similarity between verb senses in Spanish. Specifically, we compare the similarity between verb senses based on their argument structure, which is captured through semantic roles, with their similarity defined by word associations. We address the question of whether verb argument structure, which reflects the expression of the events, and word associations, which are related to the speakers' organization of the mental lexicon, shape similarity between verbs in a congruent manner, a topic which has not been explored previously. While we find significant correlations between verb sense similarities obtained from these two approaches, our findings also highlight some discrepancies between them and the importance of the degree of abstraction of the corpus annotation and psycholinguistic representations.
引用
收藏
页码:275 / 307
页数:33
相关论文
共 50 条
  • [21] An annotated corpus with support verb constructions in Portuguese
    Rassi, Amanda Pontes
    Baptista, Jorge
    Vale, Oto Araujo
    GRAGOATA-UFF, 2015, 20 (38): : 207 - 230
  • [22] Psycholinguistic measures for German verb pairs: Semantic transparency, semantic relatedness, verb family size, and age of reading acquisition
    Smolka, Eva
    Eulitz, Carsten
    BEHAVIOR RESEARCH METHODS, 2018, 50 (04) : 1540 - 1562
  • [23] Comparing and combining semantic verb classifications
    Culo, Oliver
    Erk, Katrin
    Pado, Sebastian
    Schulte im Walde, Sabine
    LANGUAGE RESOURCES AND EVALUATION, 2008, 42 (03) : 265 - 291
  • [24] Comparing Verb Synonym Resources for Portuguese
    Teixeira, Jorge
    Sarmento, Luis
    Oliveira, Eugenio
    COMPUTATIONAL PROCESSING OF THE PORTUGUESE LANGUAGE, PROCEEDINGS, 2010, 6001 : 100 - 109
  • [25] Psycholinguistic measures for German verb pairs: Semantic transparency, semantic relatedness, verb family size, and age of reading acquisition
    Eva Smolka
    Carsten Eulitz
    Behavior Research Methods, 2018, 50 : 1540 - 1562
  • [26] Comparing and combining semantic verb classifications
    Oliver Čulo
    Katrin Erk
    Sebastian Padó
    Sabine Schulte im Walde
    Language Resources and Evaluation, 2008, 42 : 265 - 291
  • [27] Comparing the University of South Florida Homograph Norms with empirical corpus data
    Rapp, Reinhard
    DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS, 2008, : 611 - 618
  • [28] A French Corpus for Semantic Similarity
    Cardon, Remi
    Grabar, Natalia
    PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020), 2020, : 6889 - 6894
  • [29] Online Corpus Construction of English Text Collection, Data Cleaning, and Similarity Analysis
    Wang, Huanyu
    MOBILE INFORMATION SYSTEMS, 2022, 2022
  • [30] Verb Sense Annotation in News Texts in the CSTNews Corpus
    Sobrevilla Cabezudo, Marco Antonio
    Maziero, Erick Galani
    da Cruz Souza, Jackson Wilke
    Dias, Myrcio de Souza
    Figueira Cardoso, Paula Christina
    Balage Filho, Pedro Paulo
    Agostini, Veronica
    Asevedo Nobrega, Fernando Antonio
    de Barros, Claudia Dias
    Di Felippo, Ariani
    Salgueiro Pardo, Thiago Alexandre
    REVISTA DE ESTUDOS DA LINGUAGEM, 2015, 23 (03) : 797 - 832