Publication:
Learning grammatical categories using paradigmatic representations: substitute words for language acquisition

Placeholder

School / College / Institute

Program

KU Authors

Co-Authors

Yatbaz, Mehmet Ali
Cirik, Volkan

Publication Date

Language

Embargo Status

Journal Title

Journal ISSN

Volume Title

Alternative Title

Abstract

Learning word categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.

Source

Publisher

Association for Computational Linguistics (ACL)

Subject

Language, Communication

Citation

Has Part

Source

COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

Book Series Title

Edition

DOI

item.page.datauri

Rights

Copyrights Note

Endorsement

Review

Supplemented By

Referenced By

0

Views

0

Downloads