@inproceedings{li-etal-2017-investigating,
title = "Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings",
author = "Li, Bofang and
Liu, Tao and
Zhao, Zhe and
Tang, Buzhou and
Drozd, Aleksandr and
Rogers, Anna and
Du, Xiaoyong",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1257",
doi = "10.18653/v1/D17-1257",
pages = "2421--2431",
abstract = "The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.",
}
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<abstract>The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.</abstract>
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%0 Conference Proceedings
%T Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
%A Li, Bofang
%A Liu, Tao
%A Zhao, Zhe
%A Tang, Buzhou
%A Drozd, Aleksandr
%A Rogers, Anna
%A Du, Xiaoyong
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F li-etal-2017-investigating
%X The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.
%R 10.18653/v1/D17-1257
%U https://aclanthology.org/D17-1257
%U https://doi.org/10.18653/v1/D17-1257
%P 2421-2431
Markdown (Informal)
[Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings](https://aclanthology.org/D17-1257) (Li et al., EMNLP 2017)
ACL