Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.
Zhengzhong Liu, Teruko Mitamura, and Eduard Hovy. 2018.Graph Based Decoding for Event Sequencing and Coreference Resolution. InProceedings of the 27th International Conference on Computational Linguistics, pages 3645–3657, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
@inproceedings{liu-etal-2018-graph, title = "Graph Based Decoding for Event Sequencing and Coreference Resolution", author = "Liu, Zhengzhong and Mitamura, Teruko and Hovy, Eduard", editor = "Bender, Emily M. and Derczynski, Leon and Isabelle, Pierre", booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/C18-1309/", pages = "3645--3657", abstract = "Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations."}
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%0 Conference Proceedings%T Graph Based Decoding for Event Sequencing and Coreference Resolution%A Liu, Zhengzhong%A Mitamura, Teruko%A Hovy, Eduard%Y Bender, Emily M.%Y Derczynski, Leon%Y Isabelle, Pierre%S Proceedings of the 27th International Conference on Computational Linguistics%D 2018%8 August%I Association for Computational Linguistics%C Santa Fe, New Mexico, USA%F liu-etal-2018-graph%X Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.%U https://aclanthology.org/C18-1309/%P 3645-3657
Zhengzhong Liu, Teruko Mitamura, and Eduard Hovy. 2018.Graph Based Decoding for Event Sequencing and Coreference Resolution. InProceedings of the 27th International Conference on Computational Linguistics, pages 3645–3657, Santa Fe, New Mexico, USA. Association for Computational Linguistics.