COBERT: COVID-19 Question Answering System Using BERT

Alzubi, Jafar A.; Jain, Rachna; Singh, Anubhav; Parwekar, Pritee; Gupta, Meenu

doi:10.1007/s13369-021-05810-5

Cited by 61 publications

(37 citation statements)

References 17 publications

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“…LitCovid , is another open-source dataset that provides centralized access to over 20,000 (and growing) PubMed 2 publications relevant to COVID-19. These datasets are being used for a range of tasks, such as text summarization (Song and Wang 2020), document search (Esteva et al 2021) and question answering systems (Alzubi et al 2021;Tang et al 2020).…”

Section: We Create a Covid-19 Publication Dataset By Curating It Thro...mentioning

confidence: 99%

“…These software applications allow users to quickly and easily locate content that is truly relevant or valuable, without having to wade through a plethora of irrelevant documents (research papers, reports). There are some question-answering systems (Alzubi et al 2021;Ngai et al 2021;Tang et al 2020) lately released to filter large amounts of publications data to respond to COVID-19 topics, however, the majority of them focus on pre and/or mid-covid literature. We are taking this research a step further, by also including the literature related to long-term COVID (in addition to pre/mid-COVID-19 issues), as well as investigating the impacts of COVID-19 on public health.…”

Section: Introductionmentioning

confidence: 99%

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A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

Raza¹

2022

Healthcare Analytics

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Section: We Create a Covid-19 Publication Dataset By Curating It Thro...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

Raza¹

2022

Healthcare Analytics

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“…These important words are known as named entities (NEs), and the task is known as named-entity recognition (NER). The task of named-entity recognition is important because it further helps in different natural language processing (NLP) tasks such as question answering [2], machine translation [3], relation extraction [4], and many more [5,6]. It is often the case that one entity resides within or overlaps with another entity.…”

Section: Introductionmentioning

confidence: 99%

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

et al. 2022

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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches.

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“…During this pandemic, artificial intelligence (AI) has served as an enabler to combat COVID-19, such as successful attempts in predicting epidemic trends [ 2 ] with sophisticated models, accelerating computer tomography detection [ 3 ] for more efficient diagnosis by computer vision, participating in drug development [ 4 ], and automatically answering epidemic-related natural language questions [ 5 - 7 ]. Besides deep learning, the knowledge graph (KG) concept has drawn increasing attention from both academia and industry since it was first proposed by Google in 2012.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

Wang¹,

Du²,

Qi³

et al. 2022

JMIR Med Inform

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Background With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. Objective To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. Methods In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. Results OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. Conclusions A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.

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COBERT: COVID-19 Question Answering System Using BERT

Cited by 61 publications

References 17 publications

A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

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