Foundations of statistical natural language processing
Foundations of statistical natural language processing
Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Automatic identification of non-compositional phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A statistical approach to the semantics of verb-particles
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Detecting a continuum of compositionality in phrasal verbs
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
An empirical model of multiword expression decomposability
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Measuring the relative compositionality of verb-noun (V-N) collocations by integrating features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
ASSIST: automated semantic assistance for translators
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Statistics for People Who (Think They) Hate Statistics: Excel 2007 Edition
Statistics for People Who (Think They) Hate Statistics: Excel 2007 Edition
Comparing and combining a semantic tagger and a statistical tool for MWE extraction
Computer Speech and Language
Semantics-based multiword expression extraction
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Hi-index | 0.00 |
This paper reports on an experiment in which we explore a new approach to the automatic measurement of multi-word expression (MWE) compositionality. We propose an algorithm which ranks MWEs by their compositionality relative to a semantic field taxonomy based on the Lancaster English semantic lexicon (Piao et al., 2005a). The semantic information provided by the lexicon is used for measuring the semantic distance between a MWE and its constituent words. The algorithm is evaluated both on 89 manually ranked MWEs and on McCarthy et al's (2003) manually ranked phrasal verbs. We compared the output of our tool with human judgments using Spearman's rank-order correlation coefficient. Our evaluation shows that the automatic ranking of the majority of our test data (86.52%) has strong to moderate correlation with the manual ranking while wide discrepancy is found for a small number of MWEs. Our algorithm also obtained a correlation of 0.3544 with manual ranking on McCarthy et al's test data, which is comparable or better than most of the measures they tested. This experiment demonstrates that a semantic lexicon can assist in MWE compositionality measurement in addition to statistical algorithms.