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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2303.14452 |
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| _version_ | 1866929484694814720 |
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| author | Zhang, Meiru Su, Yixuan Meng, Zaiqiao Fu, Zihao Collier, Nigel |
| author_facet | Zhang, Meiru Su, Yixuan Meng, Zaiqiao Fu, Zihao Collier, Nigel |
| contents | Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word. To solve this task, we propose a new framework, called COFFEE, which extracts the events solely based on the document context without referring to any oracle information. In particular, a contrastive selection model is introduced in COFFEE to rectify the generated triggers and handle multi-event instances. The proposed COFFEE outperforms state-of-the-art approaches under the oracle-free setting of the event extraction task, as evaluated on a public event extraction benchmark ACE05. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_14452 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | COFFEE: A Contrastive Oracle-Free Framework for Event Extraction Zhang, Meiru Su, Yixuan Meng, Zaiqiao Fu, Zihao Collier, Nigel Computation and Language Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word. To solve this task, we propose a new framework, called COFFEE, which extracts the events solely based on the document context without referring to any oracle information. In particular, a contrastive selection model is introduced in COFFEE to rectify the generated triggers and handle multi-event instances. The proposed COFFEE outperforms state-of-the-art approaches under the oracle-free setting of the event extraction task, as evaluated on a public event extraction benchmark ACE05. |
| title | COFFEE: A Contrastive Oracle-Free Framework for Event Extraction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2303.14452 |