WiRe57 : A Fine-Grained Benchmark for Open Information Extraction

William Lechelle, Fabrizio Gotti, Phillippe Langlais


Abstract
We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including coreference and granularity, and we take steps toward addressing inference, a significant problem. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.
Anthology ID:
W19-4002
Volume:
Proceedings of the 13th Linguistic Annotation Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | LAW | WS
SIG:
SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
6–15
Language:
URL:
https://aclanthology.org/W19-4002
DOI:
10.18653/v1/W19-4002
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W19-4002.pdf
Code
 rali-udem/WiRe57
Data
QA-SRL