@inproceedings{kushilevitz-etal-2020-two,
title = "A Two-Stage Masked {LM} Method for Term Set Expansion",
author = "Kushilevitz, Guy and
Markovitch, Shaul and
Goldberg, Yoav",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.610",
doi = "10.18653/v1/2020.acl-main.610",
pages = "6829--6835",
abstract = "We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical utility, and also of theoretical utility as it requires generalization from few examples. Previous approaches to the TSE task can be characterized as either distributional or pattern-based. We harness the power of neural masked language models (MLM) and propose a novel TSE algorithm, which combines the pattern-based and distributional approaches. Due to the small size of the seed set, fine-tuning methods are not effective, calling for more creative use of the MLM. The gist of the idea is to use the MLM to first mine for informative patterns with respect to the seed set, and then to obtain more members of the seed class by generalizing these patterns. Our method outperforms state-of-the-art TSE algorithms. Implementation is available at: \url{https://github.com/guykush/TermSetExpansion-MPB/}",
}
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<abstract>We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical utility, and also of theoretical utility as it requires generalization from few examples. Previous approaches to the TSE task can be characterized as either distributional or pattern-based. We harness the power of neural masked language models (MLM) and propose a novel TSE algorithm, which combines the pattern-based and distributional approaches. Due to the small size of the seed set, fine-tuning methods are not effective, calling for more creative use of the MLM. The gist of the idea is to use the MLM to first mine for informative patterns with respect to the seed set, and then to obtain more members of the seed class by generalizing these patterns. Our method outperforms state-of-the-art TSE algorithms. Implementation is available at: https://github.com/guykush/TermSetExpansion-MPB/</abstract>
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%0 Conference Proceedings
%T A Two-Stage Masked LM Method for Term Set Expansion
%A Kushilevitz, Guy
%A Markovitch, Shaul
%A Goldberg, Yoav
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kushilevitz-etal-2020-two
%X We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical utility, and also of theoretical utility as it requires generalization from few examples. Previous approaches to the TSE task can be characterized as either distributional or pattern-based. We harness the power of neural masked language models (MLM) and propose a novel TSE algorithm, which combines the pattern-based and distributional approaches. Due to the small size of the seed set, fine-tuning methods are not effective, calling for more creative use of the MLM. The gist of the idea is to use the MLM to first mine for informative patterns with respect to the seed set, and then to obtain more members of the seed class by generalizing these patterns. Our method outperforms state-of-the-art TSE algorithms. Implementation is available at: https://github.com/guykush/TermSetExpansion-MPB/
%R 10.18653/v1/2020.acl-main.610
%U https://aclanthology.org/2020.acl-main.610
%U https://doi.org/10.18653/v1/2020.acl-main.610
%P 6829-6835
Markdown (Informal)
[A Two-Stage Masked LM Method for Term Set Expansion](https://aclanthology.org/2020.acl-main.610) (Kushilevitz et al., ACL 2020)
ACL
- Guy Kushilevitz, Shaul Markovitch, and Yoav Goldberg. 2020. A Two-Stage Masked LM Method for Term Set Expansion. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6829–6835, Online. Association for Computational Linguistics.