Event Extraction in Complex Sentences Based on Dependency Parsing and Longformer

Li Lin, Chen Ziyang, Liao Shuxing, Du Yibin, Wu Hongxiao, Li Zhihao
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:1-7, 2024.

Abstract

Event extraction involves the identification and extraction of specific event-related information from a large corpus of textual data. In recent years, the introduction of pre-trained models has significantly enhanced the ability of these models to comprehend the semantics of sentences, leading to continuous advancements in event extraction methods. However, when it comes to long and complex sentences, these models have shown limited performance. This limitation can be attributed to the intricate structures of such sentences, which hinder the models’ ability to grasp their semantic meaning. To tackle this challenge, we propose a novel model that combines dependency analysis tools with the Longformer pre-trained model. By effectively analyzing the structures of complex sentences, our model aims to enhance the semantic understanding of these sentences. Experimental results using the ACE2005 dataset demonstrate the improved performance of our model in event extraction for complex sentences.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-lin24a, title = {Event Extraction in Complex Sentences Based on Dependency Parsing and Longformer}, author = {Lin, Li and Ziyang, Chen and Shuxing, Liao and Yibin, Du and Hongxiao, Wu and Zhihao, Li}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {1--7}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/lin24a/lin24a.pdf}, url = {https://proceedings.mlr.press/v245/lin24a.html}, abstract = {Event extraction involves the identification and extraction of specific event-related information from a large corpus of textual data. In recent years, the introduction of pre-trained models has significantly enhanced the ability of these models to comprehend the semantics of sentences, leading to continuous advancements in event extraction methods. However, when it comes to long and complex sentences, these models have shown limited performance. This limitation can be attributed to the intricate structures of such sentences, which hinder the models’ ability to grasp their semantic meaning. To tackle this challenge, we propose a novel model that combines dependency analysis tools with the Longformer pre-trained model. By effectively analyzing the structures of complex sentences, our model aims to enhance the semantic understanding of these sentences. Experimental results using the ACE2005 dataset demonstrate the improved performance of our model in event extraction for complex sentences.} }
Endnote
%0 Conference Paper %T Event Extraction in Complex Sentences Based on Dependency Parsing and Longformer %A Li Lin %A Chen Ziyang %A Liao Shuxing %A Du Yibin %A Wu Hongxiao %A Li Zhihao %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-lin24a %I PMLR %P 1--7 %U https://proceedings.mlr.press/v245/lin24a.html %V 245 %X Event extraction involves the identification and extraction of specific event-related information from a large corpus of textual data. In recent years, the introduction of pre-trained models has significantly enhanced the ability of these models to comprehend the semantics of sentences, leading to continuous advancements in event extraction methods. However, when it comes to long and complex sentences, these models have shown limited performance. This limitation can be attributed to the intricate structures of such sentences, which hinder the models’ ability to grasp their semantic meaning. To tackle this challenge, we propose a novel model that combines dependency analysis tools with the Longformer pre-trained model. By effectively analyzing the structures of complex sentences, our model aims to enhance the semantic understanding of these sentences. Experimental results using the ACE2005 dataset demonstrate the improved performance of our model in event extraction for complex sentences.
APA
Lin, L., Ziyang, C., Shuxing, L., Yibin, D., Hongxiao, W. & Zhihao, L.. (2024). Event Extraction in Complex Sentences Based on Dependency Parsing and Longformer. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:1-7 Available from https://proceedings.mlr.press/v245/lin24a.html.

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