Token-Level Multilingual Epidemic Dataset for Event Extraction

Mutuvi, Stephen; Boroş, Emanuela; Doucet, Antoine; Lejeune, Gaël; Jatowt, Adam; Odeo, Moses

doi:10.1007/978-3-030-86324-1_6

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Guo et al [8] designed a three-stage pipeline method to extract epidemic events, achieving canto-level extraction. Mutuvi et al [30] also propose a strong baseline and present a token-level dataset for multilingual epidemic EE.…”

Section: Introductionmentioning

confidence: 99%

Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

et al. 2023

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In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named multi-model fusion-based hierarchical event extraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.

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Section: Introductionmentioning

confidence: 99%

Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

et al. 2023

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Multilingual Epidemic Event Extraction

Mutuvi

Boroş

Doucet

et al. 2021

Lecture Notes in Computer Science

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In this paper, we focus on epidemic event extraction in multilingual and low-resource settings. The task of extracting epidemic events is defined as the detection of disease names and locations in a document. We experiment with a multilingual dataset comprising news articles from the medical domain with diverse morphological structures (Chinese, English, French, Greek, Polish, and Russian). We investigate various Transformer-based models, also adopting a two-stage strategy, first finding the documents that contain events and then performing event extraction. Our results show that error propagation to the downstream task was higher than expected. We also perform an in-depth analysis of the results, concluding that different entity characteristics can influence the performance. Moreover, we perform several preliminary experiments for the low-resourced languages present in the dataset using the mean teacher semi-supervised technique. Our findings show the potential of pre-trained language models benefiting from the incorporation of unannotated data in the training process.

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Token-Level Multilingual Epidemic Dataset for Event Extraction

Cited by 2 publications

References 11 publications

Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

Multilingual Epidemic Event Extraction

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