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Title
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Which distributional cues help the most? Unsupervised context selection for lexical category acquisition
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Author
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Abstract
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| Starting from the distributional bootstrapping hypothesis, we propose an unsupervised model that selects the most useful distributional information according to its salience in the input, incorporating psycholinguistic evidence. With a supervised Parts-of-Speech tagging experiment, we provide preliminary results suggesting that the distributional contexts extracted by our model yield similar performances as compared to current approaches from the literature, with a gain in psychological plausibility. We also introduce a more principled way to evaluate the effectiveness of distributional contexts in helping learners to group words in syntactic categories. |
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Language
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English
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Source (book)
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Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
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Publication
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Lisbon
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Association for Computational Linguistics
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2015
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ISBN
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978-1-941643-32-7
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Volume/pages
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p. 33-39
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Full text (open access)
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