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Measuring MWE compositionality using semantic annotation

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Measuring MWE compositionality using semantic annotation. / Piao, Scott; Rayson, Paul; Mudraya, Olga; Wilson, Andrew; Garside, Roger.

MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties. Stroudsburg : Association for Computational Linguistics, 2006. p. 2-11.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Piao, S, Rayson, P, Mudraya, O, Wilson, A & Garside, R 2006, Measuring MWE compositionality using semantic annotation. in MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties. Association for Computational Linguistics, Stroudsburg, pp. 2-11, COLING/ACL workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties, Sydney, Australia, 23/07/06. https://doi.org/10.3115/1613692.1613695

APA

Piao, S., Rayson, P., Mudraya, O., Wilson, A., & Garside, R. (2006). Measuring MWE compositionality using semantic annotation. In MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties (pp. 2-11). Association for Computational Linguistics. https://doi.org/10.3115/1613692.1613695

Vancouver

Piao S, Rayson P, Mudraya O, Wilson A, Garside R. Measuring MWE compositionality using semantic annotation. In MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties. Stroudsburg: Association for Computational Linguistics. 2006. p. 2-11 https://doi.org/10.3115/1613692.1613695

Author

Piao, Scott ; Rayson, Paul ; Mudraya, Olga ; Wilson, Andrew ; Garside, Roger. / Measuring MWE compositionality using semantic annotation. MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties. Stroudsburg : Association for Computational Linguistics, 2006. pp. 2-11

Bibtex

@inproceedings{cb194d056d04437da57b403d9cf67447,
title = "Measuring MWE compositionality using semantic annotation",
abstract = "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.",
keywords = "cs_eprint_id, 1290 cs_uid, 1",
author = "Scott Piao and Paul Rayson and Olga Mudraya and Andrew Wilson and Roger Garside",
year = "2006",
month = jul,
doi = "10.3115/1613692.1613695",
language = "English",
isbn = "1932432841",
pages = "2--11",
booktitle = "MWE '06 Proceedings of the Workshop on Multiword Expressions",
publisher = "Association for Computational Linguistics",
note = "COLING/ACL workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties ; Conference date: 23-07-2006",

}

RIS

TY - GEN

T1 - Measuring MWE compositionality using semantic annotation

AU - Piao, Scott

AU - Rayson, Paul

AU - Mudraya, Olga

AU - Wilson, Andrew

AU - Garside, Roger

PY - 2006/7

Y1 - 2006/7

N2 - 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.

AB - 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.

KW - cs_eprint_id

KW - 1290 cs_uid

KW - 1

U2 - 10.3115/1613692.1613695

DO - 10.3115/1613692.1613695

M3 - Conference contribution/Paper

SN - 1932432841

SP - 2

EP - 11

BT - MWE '06 Proceedings of the Workshop on Multiword Expressions

PB - Association for Computational Linguistics

CY - Stroudsburg

T2 - COLING/ACL workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties

Y2 - 23 July 2006

ER -