Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research

Verspoor, Karin; Šuster, Simon; Otmakhova, Yulia; Mendis, Shevon; Zhai, Zenan; Fang, Biaoyan; Lau, Jey Han; Baldwin, Timothy; Jimeno-Yepes, Antonio; Martínez, David

doi:10.1007/978-3-030-72240-1_65

Cited by 15 publications

(14 citation statements)

References 19 publications

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“…Most COVID-19 related datasets are constructed from two types of sources. The first one is scientific publications, including the datasets CORD-19 (Wang et al, 2020) and LitCovid (Chen et al, 2020), that help facilitate many types of research works, such as building search engines to retrieve relevant information from scholarly articles (Esteva et al, 2020;Zhang et al, 2020;Verspoor et al, 2020), question answering and summarization (Lee et al, 2020;Su et al, 2020). Recently, Colic et al (2020) fine-tune a BERT-based NER model on the CRAFT corpus (Verspoor et al, 2012) to recognize and then normalize biomedical ontology and terminology entities in LitCovid.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Named Entity Recognition for Vietnamese

Truong¹,

Dao²,

Nguyen³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other than English. In this paper, we present the first manuallyannotated COVID-19 domain-specific dataset for Vietnamese. Particularly, our dataset is annotated for the named entity recognition (NER) task with newly-defined entity types that can be used in other future epidemics. Our dataset also contains the largest number of entities compared to existing Vietnamese NER datasets. We empirically conduct experiments using strong baselines on our dataset, and find that: automatic Vietnamese word segmentation helps improve the NER results and the highest performances are obtained by finetuning pre-trained language models where the monolingual model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) produces higher results than the multilingual model XLM-R

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Section: Related Workmentioning

confidence: 99%

COVID-19 Named Entity Recognition for Vietnamese

Truong¹,

Dao²,

Nguyen³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…COVID-19 IE Recent work (Verspoor et al, 2020a) has focused on extracting information from the CORD-19 corpus (Wang et al, 2020b). PICO concepts are extracted and visualized in an exploratory interface in the COVID-SEE system (Verspoor et al, 2020b). In Wang et al (2020a), genes, diseases, chemicals and organisms are extracted and linked to existing biomedical KBs with information such as gene-disease relations.…”

Section: Information Extraction From Scientific Textsmentioning

confidence: 99%

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Hope¹,

Amini²,

Wadden³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms-a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available. 1 * * Equal contribution. 1 https://covidmechanisms.apps.allenai.org/ … a deep learning framework for design of antiviral candidate drugs Temperature increase can facilitate the destruction of SARS-COV-2 gpl16 antiserum blocks binding of virions to cellular receptors ...food price inflation is an unintended consequence of COVID-19 containment measures Retrieved from CORD-19 papers Ent1: deep learning Ent2: drugs Query mechanism relations

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“…It only provides term-level search and no visualization functionality. COVID-SEE 12 , proposed by Verspoor et al (2020), supports the search from CORD-19 dataset and visualization of article topics and relational concepts. Most other visualizations, however, relate to epidemiological statistics and the effects of Covid-19 on social and health factors 13 .…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

2021

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Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research

Cited by 15 publications

References 19 publications

COVID-19 Named Entity Recognition for Vietnamese

COVID-19 Named Entity Recognition for Vietnamese

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

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