Title
Which distributional cues help the most? Unsupervised context selection for lexical category acquisition
Author
Faculty/Department
Faculty of Arts. Linguistics and Literature
Publication type
conferenceObject
Publication
Lisbon :Association for Computational Linguistics, [*]
Subject
Linguistics
Source (book)
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
ISBN - Hoofdstuk
978-1-941643-32-7
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/f71fc8/128308.pdf
https://repository.uantwerpen.be/docman/irua/bd658c/128308.pdf
Handle