@inproceedings{yang-etal-2017-simple,
title = "A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings",
author = "Yang, Wei and
Lu, Wei and
Zheng, Vincent",
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/D17-1312",
doi = "10.18653/v1/D17-1312",
pages = "2898--2904",
abstract = "Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is less explored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.",
}
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%0 Conference Proceedings
%T A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
%A Yang, Wei
%A Lu, Wei
%A Zheng, Vincent
%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 yang-etal-2017-simple
%X Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is less explored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.
%R 10.18653/v1/D17-1312
%U https://aclanthology.org/D17-1312
%U https://doi.org/10.18653/v1/D17-1312
%P 2898-2904
Markdown (Informal)
[A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings](https://aclanthology.org/D17-1312) (Yang et al., EMNLP 2017)
ACL