Abstract
Creating annotated frame lexicons such as PropBank and FrameNet is expensive and labor intensive. We present a method to induce an embedded frame lexicon in an minimally supervised fashion using nothing more than unlabeled predicate-argument word pairs. We hypothesize that aggregating such pair selectional preferences across training leads us to a global understanding that captures predicate-argument frame structure. Our approach revolves around a novel integration between a predictive embedding model and an Indian Buffet Process posterior regularizer. We show, through our experimental evaluation, that we outperform baselines on two tasks and can learn an embedded frame lexicon that is able to capture some interesting generalities in relation to hand-crafted semantic frames.- Anthology ID:
- S17-1025
- Volume:
- Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Nancy Ide, Aurélie Herbelot, Lluís Màrquez
- Venue:
- *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 209–219
- Language:
- URL:
- https://aclanthology.org/S17-1025
- DOI:
- 10.18653/v1/S17-1025
- Cite (ACL):
- Sujay Kumar Jauhar and Eduard Hovy. 2017. Embedded Semantic Lexicon Induction with Joint Global and Local Optimization. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 209–219, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Embedded Semantic Lexicon Induction with Joint Global and Local Optimization (Jauhar & Hovy, *SEM 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S17-1025.pdf
- Data
- FrameNet