Attention-based Unsupervised Keyphrase Extraction and Phrase Graph for COVID-19 Medical Literature Retrieval
ACM Transactions on Computing for Healthcare
; 3(1), 2022.
Article
in English
| Scopus | ID: covidwho-1741688
ABSTRACT
Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks-BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
COVID-19; deep learning; Keyphrase extraction; medical information retrieval; Bioinformatics; Data mining; Extraction; Information retrieval; Search engines; Biomedical search engines; Key-phrase; Key-phrases extractions; Literature retrieval; Medical information; Medical literatures; Text collection; Natural language processing systems
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
ACM Transactions on Computing for Healthcare
Year:
2022
Document Type:
Article
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