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Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting : a review and empirical validation

Pawel Mandera (UGent) , Emmanuel Keuleers (UGent) and Marc Brysbaert (UGent)
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Abstract
Recent developments in distributional semantics (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013) include a new class of prediction based models that are trained on a text corpus and that measure semantic similarity between words. We discuss the relevance of these models for psycholinguistic theories and compare them to more traditional distributional semantic models. We compare the models' performances on a large dataset of semantic priming (Hutchison et al., 2013) and on a number of other tasks involving semantic processing and conclude that the prediction-based models usually offer a better fit to behavioral data. Theoretically, we argue that these models bridge the gap between traditional approaches to distributional semantics and psychologically plausible learning principles. As an aid to researchers, we release semantic vectors for English and Dutch for a range of models together with a convenient interface that can be used to extract a great number of semantic similarity measures. (C) 2016 Elsevier Inc. All rights reserved.
Keywords
WORD COOCCURRENCE STATISTICS, REPRESENTATIONS, ENGLISH, NORMS, CONSTRAINTS, INFORMATION, ACQUISITION, FREQUENCIES, SELECTION, NETWORKS, Semantic model, Distributional semantics, Semantic priming, Psycholinguistic resource

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MLA
Mandera, Pawel, Emmanuel Keuleers, and Marc Brysbaert. “Explaining Human Performance in Psycholinguistic Tasks with Models of Semantic Similarity Based on Prediction and Counting : a Review and Empirical Validation.” JOURNAL OF MEMORY AND LANGUAGE 92 (2017): 57–78. Print.
APA
Mandera, P., Keuleers, E., & Brysbaert, M. (2017). Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting : a review and empirical validation. JOURNAL OF MEMORY AND LANGUAGE, 92, 57–78.
Chicago author-date
Mandera, Pawel, Emmanuel Keuleers, and Marc Brysbaert. 2017. “Explaining Human Performance in Psycholinguistic Tasks with Models of Semantic Similarity Based on Prediction and Counting : a Review and Empirical Validation.” Journal of Memory and Language 92: 57–78.
Chicago author-date (all authors)
Mandera, Pawel, Emmanuel Keuleers, and Marc Brysbaert. 2017. “Explaining Human Performance in Psycholinguistic Tasks with Models of Semantic Similarity Based on Prediction and Counting : a Review and Empirical Validation.” Journal of Memory and Language 92: 57–78.
Vancouver
1.
Mandera P, Keuleers E, Brysbaert M. Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting : a review and empirical validation. JOURNAL OF MEMORY AND LANGUAGE. 2017;92:57–78.
IEEE
[1]
P. Mandera, E. Keuleers, and M. Brysbaert, “Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting : a review and empirical validation,” JOURNAL OF MEMORY AND LANGUAGE, vol. 92, pp. 57–78, 2017.
@article{8502464,
  abstract     = {Recent developments in distributional semantics (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013) include a new class of prediction based models that are trained on a text corpus and that measure semantic similarity between words. We discuss the relevance of these models for psycholinguistic theories and compare them to more traditional distributional semantic models. We compare the models' performances on a large dataset of semantic priming (Hutchison et al., 2013) and on a number of other tasks involving semantic processing and conclude that the prediction-based models usually offer a better fit to behavioral data. Theoretically, we argue that these models bridge the gap between traditional approaches to distributional semantics and psychologically plausible learning principles. As an aid to researchers, we release semantic vectors for English and Dutch for a range of models together with a convenient interface that can be used to extract a great number of semantic similarity measures. (C) 2016 Elsevier Inc. All rights reserved.},
  author       = {Mandera, Pawel and Keuleers, Emmanuel and Brysbaert, Marc},
  issn         = {0749-596X},
  journal      = {JOURNAL OF MEMORY AND LANGUAGE},
  keywords     = {WORD COOCCURRENCE STATISTICS,REPRESENTATIONS,ENGLISH,NORMS,CONSTRAINTS,INFORMATION,ACQUISITION,FREQUENCIES,SELECTION,NETWORKS,Semantic model,Distributional semantics,Semantic priming,Psycholinguistic resource},
  language     = {eng},
  pages        = {57--78},
  title        = {Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting : a review and empirical validation},
  url          = {http://dx.doi.org/10.1016/j.jml.2016.04.001},
  volume       = {92},
  year         = {2017},
}

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