Learning Concept Abstractness Using Weak Supervision
Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
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.- Anthology ID:
- D18-1522
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4854–4859
- Language:
- URL:
- https://aclanthology.org/D18-1522
- DOI:
- 10.18653/v1/D18-1522
- Cite (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.
- Cite (Informal):
- Learning Concept Abstractness Using Weak Supervision (Rabinovich et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D18-1522.pdf