Named Entity Recognition in Scientific Texts

Abstract

The automatic extraction of crucial information from text documents has been a fundamental pursuit since the early stages of natural language processing. With the exponential growth of digital content such as news, social media, and blogs, the need for effective information extraction has become increasingly vital. This thesis presents a comprehensive study on Named Entity Recognition (NER) in scientific texts, with a specific focus on the domain of computer science. The research involved the collection and analysis of 2000 sample abstract texts from scientific literature, which were processed using an advanced algorithm to identify and extract all terms relevant to the field of computer science. The study aimed to address the challenges of accurately identifying and categorising named entities within the context of scientific literature, particularly in the specialised domain of computer science. The findings and insights from this research contribute to the advancement of NER techniques in scientific texts and have implications for various applications in information retrieval, knowledge extraction, and natural language processing within the domain of computer science.