Abstract
Frame identification is one of the key challenges for frame-semantic parsing. The goal of this task is to determine which frame best captures the meaning of a target word or phrase in a sentence. We present a new model for frame identification that uses a pre-trained transformer model to generate representations for frames and lexical units (senses) using their formal definitions in FrameNet. Our frame identification model assesses the suitability of a frame for a target word in a sentence based on the semantic coherence of their meanings. We evaluate our model on three data sets and show that it consistently achieves better performance than previous systems.- Anthology ID:
- 2021.eacl-main.206
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2429–2434
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.206
- DOI:
- 10.18653/v1/2021.eacl-main.206
- Cite (ACL):
- Tianyu Jiang and Ellen Riloff. 2021. Exploiting Definitions for Frame Identification. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2429–2434, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting Definitions for Frame Identification (Jiang & Riloff, EACL 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.eacl-main.206.pdf
- Code
- tyjiangu/fido
- Data
- FrameNet