Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916369266638848 |
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| author | Gao, Qiang Meng, Zixiang Li, Bobo Zhou, Jun Li, Fei Teng, Chong Ji, Donghong |
| author_facet | Gao, Qiang Meng, Zixiang Li, Bobo Zhou, Jun Li, Fei Teng, Chong Ji, Donghong |
| contents | Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source. This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To build a benchmark, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task. Our work builds a new line of information extraction research and will attract new research attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm Gao, Qiang Meng, Zixiang Li, Bobo Zhou, Jun Li, Fei Teng, Chong Ji, Donghong Computation and Language Artificial Intelligence Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source. This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To build a benchmark, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task. Our work builds a new line of information extraction research and will attract new research attention. |
| title | Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16021 |