Quarterly (March, June, September, December)
160 pp. per issue
6 3/4 x 10
ISSN
0891-2017
E-ISSN
1530-9312
2014 Impact factor:
1.23

Computational Linguistics

Paola Merlo, Editor
June 2014, Vol. 40, No. 2, Pages 449-468
(doi: 10.1162/COLI_a_00177)
@ 2014 Association for Computational Linguistics
Identification of Multiword Expressions by Combining Multiple Linguistic Information Sources
Article PDF (132.34 KB)
Abstract

We propose a framework for using multiple sources of linguistic information in the task of identifying multiword expressions in natural language texts. We define various linguistically motivated classification features and introduce novel ways for computing them. We then manually define interrelationships among the features, and express them in a Bayesian network. The result is a powerful classifier that can identify multiword expressions of various types and multiple syntactic constructions in text corpora. Our methodology is unsupervised and language-independent; it requires relatively few language resources and is thus suitable for a large number of languages. We report results on English, French, and Hebrew, and demonstrate a significant improvement in identification accuracy, compared with less sophisticated baselines.