{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:09:46Z","timestamp":1762956586552,"version":"3.38.0"},"reference-count":26,"publisher":"China Science Publishing & Media Ltd.","issue":"3","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"vor","delay-in-days":157,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,9,8]]},"abstract":"<jats:p>Document-level financial event extraction (DFEE) is the task of detecting events and extracting the corresponding event arguments in financial documents, which plays an important role in information extraction in the financial domain. This task is challenging as the financial documents are generally long text and event arguments of one event may be scattered in different sentences. To address this issue, we proposed a novel Prior Information Enhanced Extraction framework (PIEE) for DFEE, leveraging prior information from both event types and pre-trained language models. Specifically, PIEE consists of three components: event detection, event argument extraction, and event table filling. In event detection, we identify the event type. Then, the event type is explicitly used for event argument extraction. Meanwhile, the implicit information within language models also provides considerable cues for event arguments localization. Finally, all the event arguments are filled in an event table by a set of predefined heuristic rules. To demonstrate the effectiveness of our proposed framework, we participated in the share task of CCKS2020 Task 4-2: Document-level Event Arguments Extraction. On both Leaderboard A and Leaderboard B, PIEE took the first place and significantly outperformed the other systems.<\/jats:p>","DOI":"10.1162\/dint_a_00103","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T17:24:19Z","timestamp":1623086659000},"page":"460-476","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":7,"title":["A Prior Information Enhanced Extraction Framework for Document-level\n                    Financial Event Extraction"],"prefix":"10.3724","volume":"3","author":[{"given":"Haitao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]},{"given":"Tong","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]},{"given":"Mingtao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]},{"given":"Guoliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]},{"given":"Wenliang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}]}],"member":"2026","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"2021102914233774100_ref1","first-page":"167","article-title":"Event extraction via dynamic multi-pooling convolutional\n                        neural networks","volume-title":"Proceedings of the 53rd Annual\n                        Meeting of the Association for Computational Linguistics and the 7th\n                        International Joint Conference on Natural Language Processing 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