COV19IR : COVID-19 Domain Literature Information Retrieval
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Abstract (arXiv)
Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.
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References · 7
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2020CORD-19: The COVID-19 Open Research Datasetcited by 180
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