Executive Summary
This study presents a comprehensive approach to analyzing and exploring the landscape of research papers in the field of Artificial Intelligence (AI). By utilizing topic modeling, clustering, and classification techniques, the study aims to optimize the search for similar documents within the AI research corpus. The multi-step process combines various machine learning algorithms to uncover underlying topics, reveal the structure of the research domain, and assist researchers in efficiently discovering relevant papers. The findings demonstrate the effectiveness of these techniques in extracting semantically similar documents, offering valuable insights and showcasing the power of natural language processing in analyzing large volumes of scientific literature in AI.
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