FAST: A carefully sampled and cognitively motivated dataset for distributional semantic evaluation
Evert S, Lapesa G (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Association for Computational Linguistics (ACL)
Pages Range: 588-595
Conference Proceedings Title: CoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings
Event location: Virtual, Online
ISBN: 9781955917056
Abstract
What is the first word that comes to your mind when you hear giraffe, or damsel, or freedom? Such free associations contain a huge amount of information on the mental representations of the corresponding concepts, and are thus an extremely valuable testbed for the evaluation of semantic representations extracted from corpora. In this paper, we present FAST (Free ASsociation Tasks), a free association dataset for English rigorously sampled from two standard free association norms collections (the Edinburgh Associative Thesaurus and the University of South Florida Free Association Norms), discuss two evaluation tasks, and provide baseline results. In parallel, we discuss methodological considerations concerning the desiderata for a proper evaluation of semantic representations.
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Germany (DE)How to cite
APA:
Evert, S., & Lapesa, G. (2021). FAST: A carefully sampled and cognitively motivated dataset for distributional semantic evaluation. In Arianna Bisazza, Omri Abend (Eds.), CoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings (pp. 588-595). Virtual, Online: Association for Computational Linguistics (ACL).
MLA:
Evert, Stephanie, and Gabriella Lapesa. "FAST: A carefully sampled and cognitively motivated dataset for distributional semantic evaluation." Proceedings of the 25th Conference on Computational Natural Language Learning, CoNLL 2021, Virtual, Online Ed. Arianna Bisazza, Omri Abend, Association for Computational Linguistics (ACL), 2021. 588-595.
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