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

Intel AI Lab

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

6 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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title = "Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow",

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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Bibliographical note

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