A Hybrid Model for Spatio-Temporal Information Recognition in COVID-19 Trajectory Text
19th International Conference on Web Information Systems and Applications, WISA 2022
; 13579 LNCS:267-279, 2022.
Article
in English
| Scopus | ID: covidwho-2173751
ABSTRACT
Since the outbreak of the COVID-19 epidemic at the end of 2019, the normalization of epidemic prevention and control has become one of the core tasks of the entire country. Health self-examination by checking the trajectory of diagnosed patients has gradually become everyone's basic necessity and essential to epidemic prevention. The COVID-19 patient's spatio-temporal information helps to facilitate the self-inspection of the masses of whether their trajectory overlaps with the confirmed cases, which promotes the epidemic prevention work. This paper, proposes a named entity recognition model to automatically identify the time and place information in the COVID-19 patient trajectory text. The model consists of an ALBERT layer, a Bi-GRU layer, and a GlobalPointer layer. The previous two layers jointly focus on extracting the context's characteristics and the semantic dependencies. And the GlobalPointer layer extracts the corresponding named entities from a global perspective, which improves the recognition ability for the long-nested place and time entities. Compared to the conventional name entity recognition models, our proposed model has high effectiveness because it has a smaller parameter scale and faster training speed. We evaluate the proposed model using a dataset crawled from the official COVID-19 trajectory text. The F1-score of the model has reached 92.86%, which outperforms four traditional named entity recognition models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
19th International Conference on Web Information Systems and Applications, WISA 2022
Year:
2022
Document Type:
Article
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