Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow

Jonathan Mamou; Oren Pereg; Moshe Wasserblat; Ido Dagan; Yoav Goldberg; Alon Eirew; Yael Green; Shira Guskin; Peter Izsak; Daniel Korat

Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow

Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat

Department of Computer Science

Intel AI Lab

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used for solving real-life use cases including integration in an automated recruitment system and an issues and defects resolution system.

Original language	English
Title of host publication	COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations
Publisher	Association for Computational Linguistics (ACL)
Pages	58-62
Number of pages	5
ISBN (Electronic)	9781948087537
State	Published - 2018
Event	27th International Conference on Computational Linguistics, COLING 2018 - Santa Fe, United States Duration: 20 Aug 2018 → 26 Aug 2018

Publication series

Name	COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations

Conference

Conference	27th International Conference on Computational Linguistics, COLING 2018
Country/Territory	United States
City	Santa Fe
Period	20/08/18 → 26/08/18

Bibliographical note

Publisher Copyright:
© COLING 2018.All right reserved.

Funding

This work was supported in part by an Intel ICRI-CI grant. The authors are grateful to Sapir Tsabari from Intel AI Lab for her help in the dataset preparation.

Funders	Funder number
Intel ICRI-CI

Cite this

Mamou, J., Pereg, O., Wasserblat, M., Dagan, I., Goldberg, Y., Eirew, A., Green, Y., Guskin, S., Izsak, P., & Korat, D. (2018). Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow. In COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations (pp. 58-62). (COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations). Association for Computational Linguistics (ACL).

Mamou, Jonathan ; Pereg, Oren ; Wasserblat, Moshe et al. / Term Set Expansion based on Multi-Context Term Embeddings : an End-to-end Workflow. COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL), 2018. pp. 58-62 (COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations).

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abstract = "We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used for solving real-life use cases including integration in an automated recruitment system and an issues and defects resolution system.",

author = "Jonathan Mamou and Oren Pereg and Moshe Wasserblat and Ido Dagan and Yoav Goldberg and Alon Eirew and Yael Green and Shira Guskin and Peter Izsak and Daniel Korat",

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Mamou, J, Pereg, O, Wasserblat, M, Dagan, I , Goldberg, Y, Eirew, A, Green, Y, Guskin, S, Izsak, P & Korat, D 2018, Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow. in COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations. COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations, Association for Computational Linguistics (ACL), pp. 58-62, 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, United States, 20/08/18.

Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow. / Mamou, Jonathan; Pereg, Oren; Wasserblat, Moshe et al.
COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL), 2018. p. 58-62 (COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Mamou J, Pereg O, Wasserblat M, Dagan I , Goldberg Y, Eirew A et al. Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow. In COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations. Association for Computational Linguistics (ACL). 2018. p. 58-62. (COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings of System Demonstrations).

Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow

Abstract

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