TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction

TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction

Kuan-Hao Huang, I.-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, and Heng Ji, in Findings of the Association for Computational Linguistics: ACL (ACL-findings), 2024.

Abstract

Bib Entry

@inproceedings{Huang2024,
  title = {TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction},
  author = {Huang, Kuan-Hao and Hsu, I-Hung and Parekh, Tanmay and Xie, Zhiyu and Zhang, Zixuan and Natarajan, Prem and Chang, Kai-Wei and Peng, Nanyun and Ji, Heng},
  booktitle = {Findings of the Association for Computational Linguistics: ACL (ACL-findings)},
  year = {2024}
}

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   Kuan-Hao Huang, I.-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, and Heng Ji, in Findings of the Association for Computational Linguistics: ACL (ACL-findings), 2024.
   BibTeX Details

   @inproceedings{Huang2024,
     title = {TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction},
     author = {Huang, Kuan-Hao and Hsu, I-Hung and Parekh, Tanmay and Xie, Zhiyu and Zhang, Zixuan and Natarajan, Prem and Chang, Kai-Wei and Peng, Nanyun and Ji, Heng},
     booktitle = {Findings of the Association for Computational Linguistics: ACL (ACL-findings)},
     year = {2024}
   }
   

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   @inproceedings{huang2021document,
     title = {Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies},
     author = {Huang, Kung-Hsiang and Peng, Nanyun},
     booktitle = {The 3rd Workshop on Narrative Understanding (NAACL 2021)},
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   Full Text Slides Code Abstract BibTeX Details

   Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via a hierarchical graph representation encoded by a proposed Graph Edgeconditioned Attention Networks (GEANet). To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.

   @inproceedings{huang2020event,
     title = {Biomedical Event Extraction with Hierarchical Knowledge Graphs},
     author = {Huang, Kung-Hsiang and Yang, Mu and Peng, Nanyun},
     booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, short},
     slideslive_id = {38940169},
     year = {2020}
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   Details