article

Unsupervised compositionality prediction of nominal compounds

Authors:

Silvio Cordeiro,

Aline Villavicencio,

Carlos RamischAuthors Info & Claims

Computational Linguistics, Volume 45, Issue 1

Pages 1 - 57

https://doi.org/10.1162/coli_a_00341

Published: 01 March 2019 Publication History

Abstract

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.

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Cited By

Garcia M(2024)Training and evaluation of vector models for GalicianLanguage Resources and Evaluation10.1007/s10579-024-09740-058:4(1419-1462)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10579-024-09740-0
Wilkens RZilio LVillavicencio A(2024)Assessing linguistic generalisation in language models: a dataset for Brazilian PortugueseLanguage Resources and Evaluation10.1007/s10579-023-09664-158:1(175-201)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10579-023-09664-1
Ganguly DBhat SBiswas C(2022)“If you can’t beat them, join them”: A Word Transformation based Generalized Skip-gram for Embedding Compound WordsProceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3574318.3574346(34-42)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1145/3574318.3574346
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Unsupervised compositionality prediction of nominal compounds

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cover image Computational Linguistics

Computational Linguistics Volume 45, Issue 1

March 2019

195 pages

ISSN:0891-2017

EISSN:1530-9312

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MIT Press

Cambridge, MA, United States

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Published: 01 March 2019

Published in COLI Volume 45, Issue 1

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Cited By

Garcia M(2024)Training and evaluation of vector models for GalicianLanguage Resources and Evaluation10.1007/s10579-024-09740-058:4(1419-1462)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10579-024-09740-0
Wilkens RZilio LVillavicencio A(2024)Assessing linguistic generalisation in language models: a dataset for Brazilian PortugueseLanguage Resources and Evaluation10.1007/s10579-023-09664-158:1(175-201)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10579-023-09664-1
Ganguly DBhat SBiswas C(2022)“If you can’t beat them, join them”: A Word Transformation based Generalized Skip-gram for Embedding Compound WordsProceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3574318.3574346(34-42)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1145/3574318.3574346
Garcia MGarcía-Salido MAlonso-Ramos M(2019)Weighted Compositional Vectors for Translating Collocations Using Monolingual CorporaComputational and Corpus-Based Phraseology10.1007/978-3-030-30135-4_9(113-128)Online publication date: 25-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30135-4_9

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