Abstract
Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE^FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE^FL allows insightful conclusions to be drawn on the actual performance of OIE extractors.- Anthology ID:
- 2024.findings-acl.496
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8372–8394
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.496/
- DOI:
- 10.18653/v1/2024.findings-acl.496
- Cite (ACL):
- Fabrice Lamarche and Philippe Langlais. 2024. BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8372–8394, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark (Lamarche & Langlais, Findings 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.496.pdf