HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling
| Autor: | Sanda M. Harabagiu, Maxwell A. Weinzierl |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Event (computing) Computer science Pooling 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Task (project management) Hopfield network Identification (information) 020204 information systems Encoding (memory) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Set (psychology) computer 0105 earth and related environmental sciences |
| Zdroj: | W-NUT@EMNLP |
| DOI: | 10.18653/v1/2020.wnut-1.80 |
| Popis: | Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets. |
| Databáze: | OpenAIRE |
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