A Comparison of Vector-based Representations for Semantic Composition

William Blacoe, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible combinations of representation and composition, exhibiting varying degrees of sophistication. Some are shallow while others operate over syntactic structure, rely on parameter learning, or require access to very large corpora. We find that shallow approaches are as good as more computationally intensive alternatives with regards to two particular tests: (1) phrase similarity and (2) paraphrase detection. The sizes of the involved training corpora and the generated vectors are not as important as the fit between the meaning representation and compositional method.
Original languageEnglish
Title of host publicationEMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Place of PublicationJeju Island, Korea
PublisherAssociation for Computational Linguistics
Number of pages11
Publication statusPublished - 1 Jul 2012


Dive into the research topics of 'A Comparison of Vector-based Representations for Semantic Composition'. Together they form a unique fingerprint.

Cite this