Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset

This tutorial walks through a complete NLP pipeline for research-level mathematics.

Using the ResearchMath-14k dataset, we extract field-specific keywords with TF-IDF, generate sentence embeddings, visualize the problem landscape with UMAP, cluster with K-Means, build a semantic search engine, and train a classifier to predict each problem's open status — then surface near-duplicate problems by similarity.