Publication
Title
Which distributional cues help the most? Unsupervised context selection for lexical category acquisition
Author
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.
Language
English
Source (book)
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning / Berwick, Robert [edit.]; et al.
Publication
Lisbon : Association for Computational Linguistics, 2015
ISBN
978-1-941643-32-7
Volume/pages
p. 33-39
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 23.10.2015
Last edited 09.01.2016
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