Treffer: Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations.
Title:
Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations.
Authors:
Brown KS; Department of Pharmaceutical Sciences, Oregon State University.; School of Chemical, Biological, and Environmental Engineering, Oregon State University., Yee E; Department of Psychological Sciences, University of Connecticut., Joergensen G; Department of Psychological Sciences, University of Connecticut., Troyer M; Department of Psychology, University of Western Ontario., Saltzman E; Department of Physical Therapy, Boston University., Rueckl J; Department of Psychological Sciences, University of Connecticut., Magnuson JS; Department of Psychological Sciences, University of Connecticut.; BCBL, Basque Center on Cognition, Brain, & Language.; Ikerbasque, Basque Foundation for Science., McRae K; Department of Psychology, University of Western Ontario.
Source:
Cognitive science [Cogn Sci] 2023 May; Vol. 47 (5), pp. e13291.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 7708195 Publication Model: Print Cited Medium: Internet ISSN: 1551-6709 (Electronic) Linking ISSN: 03640213 NLM ISO Abbreviation: Cogn Sci Subsets: MEDLINE
Imprint Name(s):
Publication: 2009-: Hoboken, N.J. : Wiley-Blackwell
Original Publication: Norwood, N. J., Ablex Pub. Corp.
Original Publication: Norwood, N. J., Ablex Pub. Corp.
MeSH Terms:
References:
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Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics; Volume 1: Long Papers, Baltimore, MD (pp. 238-247).
Bommasani, R., Davis, K., & Cardie, C. (2020). Interpreting pretrained contextualized representations via reductions to static embeddings. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online (pp. 4758-4781).
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2013). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904-911.
Buchanan, E. M., Valentine, K. D., & Maxwell, N. P. (2019). English semantic feature production norms: An extended database of 4436 concepts. Behavior Research Methods, 51, 1849-1863.
Bullinaria, J., & Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39, 510-526.
Carlson, G. N., & Tanenhaus, M. K. (1988). Thematic roles and language comprehension. Syntax and Semantics, 21, 263-288.
Chronis, G., & Erk, K. (2020). When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships. Proceedings of the 24th Conference on Computational Natural Language Learning, Online (pp. 227-244).
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Connell, L., & Lynott, D. (2014). I see/hear what you mean: Semantic activation in visual word recognition depends on perceptual attention. Journal of Experimental Psychology: General, 143(2), 527.
De Deyne, S., Navarro, D. J., & Storms, G. (2013). Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations. Behavior Research Methods, 45, 480-498.
De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51, 987-1006.
Dennis, S. (2005). A memory-based theory of verbal cognition. Cognitive Science, 29, 145-193.
Desroches, A. S., Friesen, D. C., Teles, M., Korade, C. A., & Forest, E. W. (2022). The dynamics of spoken word recognition in bilinguals. Bilingualism: Language and Cognition, 25(4), 705-710.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) BERT: Pre-training of deep bidrectional transformere for language understanding. arXiv, 1810.04805.
Dresang, H. C., Dickey, M. W., & Warren, T. C. (2019). Semantic memory for objects, actions, and events: A novel test of event-related conceptual semantic knowledge. Cognitive Neuropsychology, 36, 313-335.
Erk, K., Pado, S., & Pado, U. (2010). A flexible, corpus-driven model of regular and inverse selectional preferences. Computational Linguistics, 36, 723-763.
Estes, Z., Golonka, S., & Jones, L. L. (2011). Thematic thinking: The apprehension and consequences of thematic relations. Psychology of Learning and Motivation: Advances in Research and Theory, 54, 249-294.
Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. Proceedings of the 29th Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China (pp. 55-65).
Ettinger, A. (2020). What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models. Transactions of the Association for Computational Linguistics, 8, 34-48.
Hill, F., Reichart, R., & Korhonen, A. (2015). Simlex-999: Evaluating semantic models with genuine similarity estimation. Computational Linguistics, 41, 665-695.
Fernandino, L., Tong, J. Q., Conant, L. L., Humphries, C. J., & Binder, J. R. (2022). Decoding the information structure underlying the neural representation of concepts. Proceedings of the National Academy of Sciences, 119(6), e2108091119.
Ferretti, T. R., McRae, K., & Hatherell, A. (2001). Integrating verbs, situation schemas, and thematic role concepts. Journal of Memory and Language, 44, 516-547.
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., & Ruppin, E. (2002). Placing search in context: The concept revisited. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, Hong Kong (pp. 406-414).
Gentner, D., & Boroditsky, L. (2001). Individuation, relativity, and early word learning. In Language Acquisition and Conceptual Devlopment, M. Bowerman and S. C. Levinson ed., Cambridge University Press, Cambridge, UK.
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Contributed Indexing:
Keywords: Distributional semantic models; Event-based relations; Function, shape, and color relations; Semantic relations; Thematic fit
Entry Date(s):
Date Created: 20230515 Date Completed: 20230516 Latest Revision: 20230604
Update Code:
20260130
DOI:
10.1111/cogs.13291
PMID:
37183557
Database:
MEDLINE
Weitere Informationen
Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract-concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb-noun thematic role relations and noun-noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations.
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