Semantic Changepoint Detection for Finding Potentially Novel Research Publications

How has the focus of research papers on a given disease changed over time? Identifying the papers at the cusps of change can help highlight the emergence of a new topic or a change in the direction of research. We present a generally applicable unsupervised approach to this question based on semanti...

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Detalles Bibliográficos
Autores principales: Dinakar, Bhavish, Boguslav, Mayla R., Görg, Carsten, Dinakarpandian, Deendayal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352552/
https://www.ncbi.nlm.nih.gov/pubmed/33691009
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author Dinakar, Bhavish
Boguslav, Mayla R.
Görg, Carsten
Dinakarpandian, Deendayal
author_facet Dinakar, Bhavish
Boguslav, Mayla R.
Görg, Carsten
Dinakarpandian, Deendayal
author_sort Dinakar, Bhavish
collection PubMed
description How has the focus of research papers on a given disease changed over time? Identifying the papers at the cusps of change can help highlight the emergence of a new topic or a change in the direction of research. We present a generally applicable unsupervised approach to this question based on semantic changepoints within a given collection of research papers. We illustrate the approach by a range of examples based on a nascent corpus of literature on COVID-19 as well as subsets of papers from PubMed on the World Health Organization list of neglected tropical diseases. The software is freely available at: https://github.com/pdddinakar/SemanticChangepointDetection.
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spelling pubmed-83525522021-08-09 Semantic Changepoint Detection for Finding Potentially Novel Research Publications Dinakar, Bhavish Boguslav, Mayla R. Görg, Carsten Dinakarpandian, Deendayal Pac Symp Biocomput Article How has the focus of research papers on a given disease changed over time? Identifying the papers at the cusps of change can help highlight the emergence of a new topic or a change in the direction of research. We present a generally applicable unsupervised approach to this question based on semantic changepoints within a given collection of research papers. We illustrate the approach by a range of examples based on a nascent corpus of literature on COVID-19 as well as subsets of papers from PubMed on the World Health Organization list of neglected tropical diseases. The software is freely available at: https://github.com/pdddinakar/SemanticChangepointDetection. 2021 /pmc/articles/PMC8352552/ /pubmed/33691009 Text en https://creativecommons.org/licenses/by/4.0/Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.
spellingShingle Article
Dinakar, Bhavish
Boguslav, Mayla R.
Görg, Carsten
Dinakarpandian, Deendayal
Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title_full Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title_fullStr Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title_full_unstemmed Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title_short Semantic Changepoint Detection for Finding Potentially Novel Research Publications
title_sort semantic changepoint detection for finding potentially novel research publications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352552/
https://www.ncbi.nlm.nih.gov/pubmed/33691009
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AT dinakarpandiandeendayal semanticchangepointdetectionforfindingpotentiallynovelresearchpublications

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