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This tutorial demonstrates a complete NLP pipeline for research mathematics, utilizing TF-IDF, sentence embeddings, and UMAP to build a semantic search engine and classifier for the ResearchMath-14k dataset. The approach effectively clusters problems, predicts their open status, and identifies near-duplicates, offering a practical framework for navigating large-scale mathematical corpora.
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. The post Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset appeared first on MarkTechPost.
In this tutorial, we work with the amphora/ResearchMath-14k dataset, a collection of research-level mathematics problems mined from arXiv. We load the dataset, inspect its structure, and explore how the problems are distributed across mathematical fields and open-status categories. We then move beyond basic analysis by extracting field-specific keywords, generating semantic embeddings, visualizing the…
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