Abstract: Natural Language Processing (NLP) techniques have revolutionized the extraction of valuable information from vast repositories of scientific literature. This paper aims to provide an in-depth analysis of the applications, methodologies, and challenges associated with leveraging NLP in scientific literature mining. It explores the advancements in NLP algorithms, their application in knowledge discovery, text summarization, entity recognition, and sentiment analysis within the context of scientific literature. Additionally, this paper addresses the challenges, such as domain-specific language complexities, data scarcity, and ethical considerations, while proposing potential solutions to further enhance the efficacy of NLP in scientific literature mining. The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At the same time, further and more dramatic changes have also occurred on the working environment, adding to the already existing dynamic nature of it. Natural Language Processing (NLP) refers to a collection of methods for identifying, reading, extracting and untimely transforming large collections of language. In this work, we aim to give an overview of how NLP has successfully been applied thus far in Exposome research. Methods: We conduct a literature search on PubMed, Scopus and Web of Science for scientific articles published between 2011 and 2021.
Keywords: Text Mining, Information Extraction, Scientific Document Analysis, Named Entity Recognition (NER),Topic Modeling, Sentiment Analysis, Machine Learning in NLP, Data Mining, Bioinformatics, Knowledge Discovery, natural language processing; exposure research; exposome; machine learning.
Cite:
Dr. Jaynesh H. Desai, "Natural Language Processing in Scientific Literature Mining: Advancements, Applications, and Challenges", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 2, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13214.
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DOI:
10.17148/IJARCCE.2024.13214
