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

Huang, Kung-Hsiang; Peng, Nanyun

doi:10.18653/v1/2021.nuse-1.4

Cited by 18 publications

(12 citation statements)

References 28 publications

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“…The model can deal with extended contexts that go beyond single phrases and does not require entity recognition or coreference resolution as a preprocessing step. Using DVN to capture cross-event dependencies while addressing event mention extraction, event co-reference, and entity co-reference, Huang et al [9] presented an end-to-end document-level event extraction model called DEED. DEED has a considerable advantage in the effectiveness of capturing inter-event correlations when using gradient ascent to build structured trigger predictions.…”

Section: End-to-end Modelsmentioning

confidence: 99%

Document-level Event Extraction - A Survey of Methods and Applications

Liu

Luan

Wang

et al. 2023

J. Phys.: Conf. Ser.

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Event extraction aims to detect occurrences of specified types and extract corresponding event arguments from unstructured data input, which is an integral part of information extraction [1]. Many downstream tasks, such as text summarization, causal relationship identification, and event reasoning, make extensive use of event extraction. However, most of the existing research on event extraction remains at the sentence level. Document-level event extraction is still under exploration and lacks relevant review. Focusing on the task of document-level event extraction, this survey first divides the existing technologies into three categories and introduces the most representative models; Then, we list some commonly used datasets for document-level event extraction with their usage messages; Finally, we analyse the current research gaps of document-level event extraction and future research trends.

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Section: End-to-end Modelsmentioning

confidence: 99%

Document-level Event Extraction - A Survey of Methods and Applications

Liu

Luan

Wang

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In some studies [13,14], the documentlevel event argument extraction task is considered as a populated paradigm that follows the MUC-4 task setting and is dedicated to extracting event arguments scattered in documents. In addition, Yang et al [8], Huang et al [15] and Li et al [7] follow the approach of first detecting the event type and then performing event arguments extraction. Specific event trigger words are first identified to determine the event type, and then event arguments beyond the sentence boundaries are extracted.…”

Section: Document-level Event Extractionmentioning

confidence: 99%

Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

Chu¹,

Yang²,

Wei³

et al. 2023

Preprint

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Structured extraction of emergency event information can effectively enhance the ability to re-spond to emergency events. This article focuses on the extraction of Chinese document-level emergency events, which mainly faces two key issues in this field: first, related datasets; Secondly, existing DEE (document-level event extraction) studies mostly use sequence annotation to extract candidate entities in the subtask of candidate entity extraction without considering the problem of role overlapping between candidate entities. On the one hand, this article constructs a Chinese document-level emergency extraction dataset, CDEEE, which first annotates the issues of argu-ment scattering, multiple events, and role overlapping. On the other hand, this article proposes a model RODEE for the problem of role overlapping in DEE tasks. This model first uses two in-dependent modules to represent the head and tail positions of candidate entities, then uses a multiplication attention mechanism to interact with the two to obtain a scoring matrix. Finally, role-overlapping candidate entities are predicted to assist in completing DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results showed that RODEE can effectively solve the problem of role overlapping in candidate entities and improve the performance of the DEE model.

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“…In some studies [13,14], the document-level event argument extraction task is considered a populated paradigm that follows the MUC-4 task setting and is dedicated to extracting event arguments scattered in documents. In addition, Yang et al [8], Huang et al [15], and Li et al [7] follow the approach of first detecting the event type and then performing event argument extraction. Specific event trigger words are first identified to determine the event type, and then event arguments beyond the sentence boundaries are extracted.…”

Section: Document-level Event Extractionmentioning

confidence: 99%

Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

et al. 2023

View full text Add to dashboard Cite

Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%.

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

Cited by 18 publications

References 28 publications

Document-level Event Extraction - A Survey of Methods and Applications

Document-level Event Extraction - A Survey of Methods and Applications

Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods

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