William Léchelle, Fabrizio Gotti, Philippe Langlais
We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including inference and granularity. 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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Open Information Extraction | WiRe57 | F1 | 35.8 | MinIE Gashteovski et al. (2017) |
| Open Information Extraction | WiRe57 | F1 | 34.2 | ClausIE Del Corro and Gemulla (2013) |
| Open Information Extraction | WiRe57 | F1 | 26.7 | OpenIE 4 Mausam (2016) |
| Open Information Extraction | WiRe57 | F1 | 23.9 | Ollie Mausam et al. (2012) |
| Open Information Extraction | WiRe57 | F1 | 20 | ReVerb Fader et al. (2011) |
| Open Information Extraction | WiRe57 | F1 | 19.8 | Stanford Angeli et al. (2015) |
| Open Information Extraction | WiRe57 | F1 | 18.7 | PropS Stanovsky et al. (2016) |