Abstract
Frame semantics is a well-established framework to represent the meaning of natural language in computational terms. In this work, we aim to propose a quantitative measure of relatedness between pairs of frame instances. We test our method on a dataset of sentence pairs, highlighting the correlation between our metric and human judgments of semantic similarity. Furthermore, we propose an application of our measure for clustering frame instances to extract prototypical knowledge from natural language.- Anthology ID:
- S18-2029
- Volume:
- Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Malvina Nissim, Jonathan Berant, Alessandro Lenci
- Venue:
- *SEM
- SIGs:
- SIGSEM | SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 245–254
- Language:
- URL:
- https://aclanthology.org/S18-2029
- DOI:
- 10.18653/v1/S18-2029
- Cite (ACL):
- Valerio Basile, Roque Lopez Condori, and Elena Cabrio. 2018. Measuring Frame Instance Relatedness. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 245–254, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Measuring Frame Instance Relatedness (Basile et al., *SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/S18-2029.pdf
- Data
- FrameNet