Named Entity Recognition (NER) and Relation Extraction in Scientific Publications
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Abstract
Scientific publications are essential sources of information for researchers across various fields. However, the increasing number of publications has made it challenging for researchers to keep up with the latest advancements. The task of extracting key phrases and relationships from scientific papers is of utmost importance in the field of natural language processing. This task plays a crucial role in helping researchers efficiently identify relevant articles and extract valuable insights from them. This research focuses on the problem of key phrase extraction, classification, and relationship identification in scientific publications. The problem is divided into two sub-problems: key phrase extraction and classification into PROCESS, TASK, and MATERIAL categories, and relationship identification. To address these sub-problems, advanced technologies such as Sci BERT, Mini LM Sentence Transformer, and SVM are utilized. These techniques enable efficient processing and analysis of scientific text, facilitating key phrase extraction, and classification, and relationship identification. By effectively tackling these challenges, researchers can navigate the vast amount of scientific literature more efficiently, identifying relevant articles, and uncovering valuable connections and insights within the text.
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