Open Information Extraction
In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
Papers
Showing 1–10 of 207 papers
All datasetsCaRBWiRe57OIE2016BenchIELSOIE-wikiLSOIENYTPenn TreebankWebDocOIE-healthcareDocOIE-transportationCaRB OIE benchmark (Greek Use-case)
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | SMiLe-OIE | F1 | 51.73 | — | Unverified |
| 2 | BERT + Dep-GCN - Const-GCN | F1 | 50.21 | — | Unverified |
| 3 | BERT + Dep-GCN [?] Const-GCN | F1 | 49.89 | — | Unverified |
| 4 | BERT + Const-GCN | F1 | 49.71 | — | Unverified |
| 5 | IMoJIE Kolluru et al. (2020) | F1 | 49.24 | — | Unverified |
| 6 | BERT + Dep-GCN | F1 | 48.71 | — | Unverified |
| 7 | BERT Solawetz and Larson (2021) | F1 | 47.54 | — | Unverified |
| 8 | CIGL-OIE + IGL-CA Kolluru et al. (2020) | F1 | 44.75 | — | Unverified |
| 9 | GloVe + bi-LSTM + CRF | F1 | 44.48 | — | Unverified |
| 10 | GloVe + bi-LSTM Stanovsky et al. (2018) | F1 | 43.9 | — | Unverified |