@inproceedings{tu-etal-2017-learning,
title = "Learning to Embed Words in Context for Syntactic Tasks",
author = "Tu, Lifu and
Gimpel, Kevin and
Livescu, Karen",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2632",
doi = "10.18653/v1/W17-2632",
pages = "265--275",
abstract = "We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.",
}
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%0 Conference Proceedings
%T Learning to Embed Words in Context for Syntactic Tasks
%A Tu, Lifu
%A Gimpel, Kevin
%A Livescu, Karen
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F tu-etal-2017-learning
%X We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.
%R 10.18653/v1/W17-2632
%U https://aclanthology.org/W17-2632
%U https://doi.org/10.18653/v1/W17-2632
%P 265-275
Markdown (Informal)
[Learning to Embed Words in Context for Syntactic Tasks](https://aclanthology.org/W17-2632) (Tu et al., RepL4NLP 2017)
ACL