A multilingual paradigm for automatic verb classification. Merlo, P., Stevenson, S., Tsang, V., & Allaria, G. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL-02), Philadelphia, PA, July, 2002. abstract bibtex We demonstrate the benefits of a multilingual approach to automatic lexical semantic verb classification based on statistical analysis of corpora in multiple languages. Our research incorporates two interrelated threads. In one, we exploit the similarities in the crosslinguistic classification of verbs, to extend work on English verb classification to a new language (Italian), and to new classes within that language, achieving an accuracy of 86.4% (baseline 33.9%). Our second strand of research exploits the differences across languages in the syntactic expression of semantic properties, to show that complementary information about English verbs can be extracted from their translations in a second language (Chinese), improving classification performance of the English verbs, achieving an accuracy of 83.5% (baseline 33.3%).
@InProceedings{ tsang2,
author = {Paola Merlo and Suzanne Stevenson and Vivian Tsang and
Gianluca Allaria},
title = {A multilingual paradigm for automatic verb classification},
booktitle = {Proceedings of the 40th Anniversary Meeting of the
Association for Computational Linguistics (ACL-02)},
address = {Philadelphia, PA},
month = {July},
year = {2002},
abstract = {We demonstrate the benefits of a multilingual approach to
automatic lexical semantic verb classification based on
statistical analysis of corpora in multiple languages. Our
research incorporates two interrelated threads. In one, we
exploit the <i>similarities</i> in the crosslinguistic
classification of verbs, to extend work on English verb
classification to a new language (Italian), and to new
classes within that language, achieving an accuracy of
86.4% (baseline 33.9%). Our second strand of research
exploits the <i>differences</i> across languages in the
syntactic expression of semantic properties, to show that
complementary information about English verbs can be
extracted from their translations in a second language
(Chinese), improving classification performance of the
English verbs, achieving an accuracy of 83.5% (baseline
33.3%).},
download = {http://www.cs.toronto.edu/~vyctsang/cv/papers/acl02.pdf}
}
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