The DOI matches its Crossref record; 218 of the 218 references it cites were found in a registry or an index.
Composition in Distributional Models of Semantics
Abstract
Vector‐based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector‐based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
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A Convolutional Neural Network for Modelling Sentences
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Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181
Learning Distributed Representations of Sentences from Unlabelled Data
Hill, F., Cho, K., & Korhonen, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1162 · Cited by 566
Metadata
- Authors
- Mitchell, J., & Lapata, M.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2010-11
- Volume
- 34
- Issue
- 8
- Pages
- 1388-1429
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W1984052055
- Cited by
- 960
- References
- 218
- FWCI
- 35.37
- Type
- journal article
- Language
- en
- Open access
- closed
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