Representing Sentences as Low-Rank Subspaces

Jiaqi Mu, Suma Bhat, Pramod Viswanath


Abstract
Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences – the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.
Anthology ID:
P17-2099
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
629–634
Language:
URL:
https://aclanthology.org/P17-2099
DOI:
10.18653/v1/P17-2099
Bibkey:
Cite (ACL):
Jiaqi Mu, Suma Bhat, and Pramod Viswanath. 2017. Representing Sentences as Low-Rank Subspaces. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 629–634, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Representing Sentences as Low-Rank Subspaces (Mu et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2099.pdf