Automatic Keyword Extraction from Medical and Healthcare Curriculum

2016 ◽  
Author(s):  
Martin Komenda ◽  
Matěj Karolyi ◽  
Andrea Pokorná ◽  
Martin Víta ◽  
Vincent Kríž
Keyword(s):  
Keyword Extraction ◽  
Medical And Healthcare

viewpoint TIME TO RESTART: PROTOCOL OF RESUMPTION OF ACTIVITIES OF A DERMATOLOGICAL CLINIC OF A LEVEL II HOSPITAL IN THE COVID-19 ERA. (Preprint)

2020 ◽  
Author(s):  
Federico Diotallevi ◽  
Anna Campanati ◽  
Giulia Radi ◽  
Oriana Simonetti ◽  
Emanuela Martina ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
World Health ◽  
New Wave ◽  
Staff Members ◽  
Level Ii ◽  
The World ◽  
Health Organization ◽  
Spread Of Infection ◽  
Hospital Wards ◽  
Medical And Healthcare

UNSTRUCTURED Two months have passed since the World Health Organization (WHO) declared the pandemic of the Coronavirus Disease 19 (COVID-19), caused by the SARS CoV-2 virus, on March 11, 2020. Medical and healthcare workers have continued to be on the frontline to defeat this disease, however, continual changes are being made to their working habits which are proving to be difficult. Since the beginning of the pandemic, a major reorganisation of all hospital wards, including dermatological wards, has been carried out in order to make medical and nursing staff available in COVID wards and to prevent the spread of infection. These strategies, which were also adopted in our clinic, proved to be effective, as no staff members or patients were infected by the virus. Now, thanks to the global decrease in SARS-CovV2 infections, it is necessary to make dermatological wards accessible to patients again, but it is also essential to adopt specific protocols to avoid a new wave of infections.

scholarly journals Keyword extraction method for machine reading comprehension based on natural language processing

2021 ◽  
Vol 1955 (1) ◽  
pp. 012072
Author(s):  
Ruiheng Li ◽  
Xuan Zhang ◽  
Chengdong Li ◽  
Zhongju Zheng ◽  
Zihang Zhou ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Extraction Method ◽  
Keyword Extraction ◽  
Machine Reading

scholarly journals Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation

2021 ◽  
Author(s):  
Gretel Liz De la Peña Sarracén ◽  
Paolo Rosso
Keyword(s):  
Language Model ◽  
Hybrid Approach ◽  
Attention Mechanism ◽  
Graph Representation ◽  
Keyword Extraction ◽  
Eigenvector Centrality ◽  
Model Learning ◽  
Harmful Content ◽  
The Impact ◽  
Offensive Language

AbstractThe proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.

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