Abstract
We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and used a two-layer multilayer perceptron to classify a claim as correct or not. Our submission achieved a score of 0.3966 on the Evidence F1 metric with accuracy of 44.79%, and FEVER score of 0.2628 F1 points.- Anthology ID:
- W18-5527
- Volume:
- Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–165
- Language:
- URL:
- https://aclanthology.org/W18-5527
- DOI:
- 10.18653/v1/W18-5527
- Cite (ACL):
- Ankur Padia, Francis Ferraro, and Tim Finin. 2018. Team UMBC-FEVER : Claim verification using Semantic Lexical Resources. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 161–165, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Team UMBC-FEVER : Claim verification using Semantic Lexical Resources (Padia et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-5527.pdf
- Data
- FrameNet