Modeling Human Morphological Competence
One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of mo...
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Frontiers Media S.A.
2020-11-01
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Series: | Frontiers in Psychology |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/full |
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author | Yohei Oseki Yohei Oseki Alec Marantz Alec Marantz Alec Marantz |
author_facet | Yohei Oseki Yohei Oseki Alec Marantz Alec Marantz Alec Marantz |
author_sort | Yohei Oseki |
collection | DOAJ |
description | One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora. |
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institution | Directory Open Access Journal |
issn | 1664-1078 |
language | English |
last_indexed | 2024-12-13T17:42:09Z |
publishDate | 2020-11-01 |
publisher | Frontiers Media S.A. |
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spelling | doaj.art-e282194daa26479a821a626db275b3c42022-12-21T23:36:43ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-11-011110.3389/fpsyg.2020.513740513740Modeling Human Morphological CompetenceYohei Oseki0Yohei Oseki1Alec Marantz2Alec Marantz3Alec Marantz4Faculty of Science & Engineering, Waseda University, Tokyo, JapanDepartment of Linguistics, New York University, New York, NY, United StatesDepartment of Linguistics, New York University, New York, NY, United StatesDepartment of Psychology, New York University, New York, NY, United StatesNYU Abu Dhabi Institute, New York University, Abu Dhabi, United Arab EmiratesOne of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora.https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/fullmorphologygrammaticalityacceptabilityprobabilitypsycholinguisticscomputational modeling |
spellingShingle | Yohei Oseki Yohei Oseki Alec Marantz Alec Marantz Alec Marantz Modeling Human Morphological Competence Frontiers in Psychology morphology grammaticality acceptability probability psycholinguistics computational modeling |
title | Modeling Human Morphological Competence |
title_full | Modeling Human Morphological Competence |
title_fullStr | Modeling Human Morphological Competence |
title_full_unstemmed | Modeling Human Morphological Competence |
title_short | Modeling Human Morphological Competence |
title_sort | modeling human morphological competence |
topic | morphology grammaticality acceptability probability psycholinguistics computational modeling |
url | https://www.frontiersin.org/articles/10.3389/fpsyg.2020.513740/full |
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