Joint event extraction along shortest dependency paths using graph convolutional networks

Balali, Ali; Asadpour, Masoud; Campos, Ricardo; Jatowt, Adam

doi:10.1016/j.knosys.2020.106492

Cited by 30 publications

(23 citation statements)

References 53 publications

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“…(Rao et al, 2017) proposes a subgraph matching based method to extract biomedical events from AMR graphs, while uses an additional GCN based encoder for obtaining better word representations. Besides, graph neural networks are also widely used for event extraction (Liu et al, 2018;Balali et al, 2020;Zhang et al, 2021) and relation and entity extraction (Zhang et al, 2018;Sun et al, 2020). Graph neural networks also demonstrate effectiveness to encode other types of intrinsic structures of a sentence, such as knowledge graph (Zhang et al, 2019a;, document-level relations (Sahu et al, 2019;Lockard et al, 2020;, and selfconstructed graphs (Kim and Lee, 2012;Zhu et al, 2019;Qian et al, 2019;Sahu et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction

Zhang¹,

Ji²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser. Our framework consists of two novel components: 1) an AMR based semantic graph aggregator to let the candidate entity and event trigger nodes collect neighborhood information from AMR graph for passing message among related knowledge elements; 2) an AMR guided graph decoder to extract knowledge elements based on the order decided by the hierarchical structures in AMR. Experiments on multiple datasets have shown that the AMR graph encoder and decoder have provided significant gains and our approach has achieved new state-of-the-art performance on all IE subtasks 1 .

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Section: Related Workmentioning

confidence: 99%

Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction

Zhang¹,

Ji²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Initial attempts on event extraction relied on hand-crafted features and a pipeline architecture (Ahn, 2006;Gupta and Ji, 2009;Li et al, 2013). Later studies gained significant improvement from neural approaches, especially large pre-trained language models (Wadden et al, 2019;Nguyen et al, 2016;Lin et al, 2020;Balali et al, 2020). Recently, event extraction at the document level gains more attention.…”

Section: Document-level Event Extractionmentioning

confidence: 99%

Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

Huang¹,

Peng²

2021

Proceedings of the Third Workshop on Narrative Understanding

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Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.

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“…(Ahn, 2006;Gupta and Ji, 2009;. Later studies gained significant improvement from neural approaches, especially large pre-trained language models (Wadden et al, 2019;Nguyen et al, 2016;Lin et al, 2020;Balali et al, 2020). Recently, event extraction at the document level gains more attention.…”

Section: Document-level Information Extractionmentioning

confidence: 99%

Proceedings of the Third Workshop on Narrative Understanding

2021

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While natural language understanding of longform documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text -scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised 'interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.

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Joint event extraction along shortest dependency paths using graph convolutional networks

Cited by 30 publications

References 53 publications

Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction

Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction

Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

Proceedings of the Third Workshop on Narrative Understanding

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