@inproceedings{rabinovich-etal-2018-learning,
title = "Learning Concept Abstractness Using Weak Supervision",
author = "Rabinovich, Ella and
Sznajder, Benjamin and
Spector, Artem and
Shnayderman, Ilya and
Aharonov, Ranit and
Konopnicki, David and
Slonim, Noam",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1522",
doi = "10.18653/v1/D18-1522",
pages = "4854--4859",
abstract = "We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.",
}
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<abstract>We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.</abstract>
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%0 Conference Proceedings
%T Learning Concept Abstractness Using Weak Supervision
%A Rabinovich, Ella
%A Sznajder, Benjamin
%A Spector, Artem
%A Shnayderman, Ilya
%A Aharonov, Ranit
%A Konopnicki, David
%A Slonim, Noam
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rabinovich-etal-2018-learning
%X We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.
%R 10.18653/v1/D18-1522
%U https://aclanthology.org/D18-1522
%U https://doi.org/10.18653/v1/D18-1522
%P 4854-4859
Markdown (Informal)
[Learning Concept Abstractness Using Weak Supervision](https://aclanthology.org/D18-1522) (Rabinovich et al., EMNLP 2018)
ACL
- Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, and Noam Slonim. 2018. Learning Concept Abstractness Using Weak Supervision. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4854–4859, Brussels, Belgium. Association for Computational Linguistics.