Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset
This tutorial details the creation of a semantic search engine and an open-status classifier using the ResearchMath-14k dataset, which comprises mathematical problems sourced from arXiv. The process involves loading and analyzing the dataset's structure, including the distribution of problems across various mathematical fields and open-status categories. Key steps include extracting field-specific keywords, generating semantic embeddings, visualizing the data landscape, clustering similar problems, and training a classifier to predict problem status from these embeddings. AI
IMPACT Enables new methods for organizing and querying large collections of mathematical research papers.