Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds

Pegah Alipoormolabashi, Sabine Schulte im Walde


Abstract
Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.
Anthology ID:
2020.winlp-1.13
Volume:
Proceedings of the Fourth Widening Natural Language Processing Workshop
Month:
July
Year:
2020
Address:
Seattle, USA
Editors:
Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
Venue:
WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
51–54
Language:
URL:
https://aclanthology.org/2020.winlp-1.13
DOI:
10.18653/v1/2020.winlp-1.13
Bibkey:
Cite (ACL):
Pegah Alipoormolabashi and Sabine Schulte im Walde. 2020. Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 51–54, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds (Alipoormolabashi & Schulte im Walde, WiNLP 2020)
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