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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/4313| Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision | |
| Xuan Wang. Xiangchen Song. Bangzheng Li. Yingjun Guan. Jiawei Han. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2003.12218v5.pdf | |
| We created this CORD-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This CORD-NER dataset covers 75 fine-grained entity types: In addition to the common biomedical entity types (e.g., genes, chemicals and diseases), it covers many new entity types related explicitly to the COVID-19 studies (e.g., coronaviruses, viral proteins, evolution, materials, substrates and immune responses), which may benefit research on COVID-19 related virus, spreading mechanisms, and potential vaccines. CORD-NER annotation is a combination of four sources with different NER methods. The quality of CORD-NER annotation surpasses SciSpacy (over 10% higher on the F1 score based on a sample set of documents), a fully supervised BioNER tool. Moreover, CORD-NER supports incrementally adding new documents as well as adding new entity types when needed by adding dozens of seeds as the input examples. We will constantly update CORD-NER based on the incremental updates of the CORD-19 corpus and the improvement of our system. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2003.12218v5.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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