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        <title>Learning Composition Models for Phrase Embeddings</title>
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    <abstract>Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00135</identifier>
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%0 Journal Article
%T Learning Composition Models for Phrase Embeddings
%A Yu, Mo
%A Dredze, Mark
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F yu-dredze-2015-learning
%X Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.
%R 10.1162/tacl_a_00135
%U https://aclanthology.org/Q15-1017
%U https://doi.org/10.1162/tacl_a_00135
%P 227-242

Markdown (Informal)

[Learning Composition Models for Phrase Embeddings](https://aclanthology.org/Q15-1017) (Yu & Dredze, TACL 2015)

• Learning Composition Models for Phrase Embeddings (Yu & Dredze, TACL 2015)

ACL

• Mo Yu and Mark Dredze. 2015. Learning Composition Models for Phrase Embeddings. Transactions of the Association for Computational Linguistics, 3:227–242.