@inproceedings{lee-chen-2017-muse,
title = "{MUSE}: Modularizing Unsupervised Sense Embeddings",
author = "Lee, Guang-He and
Chen, Yun-Nung",
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/D17-1034",
doi = "10.18653/v1/D17-1034",
pages = "327--337",
abstract = "This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.",
}
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%0 Conference Proceedings
%T MUSE: Modularizing Unsupervised Sense Embeddings
%A Lee, Guang-He
%A Chen, Yun-Nung
%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 lee-chen-2017-muse
%X This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.
%R 10.18653/v1/D17-1034
%U https://aclanthology.org/D17-1034
%U https://doi.org/10.18653/v1/D17-1034
%P 327-337
Markdown (Informal)
[MUSE: Modularizing Unsupervised Sense Embeddings](https://aclanthology.org/D17-1034) (Lee & Chen, EMNLP 2017)
ACL
- Guang-He Lee and Yun-Nung Chen. 2017. MUSE: Modularizing Unsupervised Sense Embeddings. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 327–337, Copenhagen, Denmark. Association for Computational Linguistics.