aiqbench takeAI Products

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.

Source coveringMarkTechPost

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

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

MarkTechPostJune 4, 20263 min read

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…

Excerpt shown under the publisher's RSS terms · 53 words · full text and figures are on the publisher's site

Read at MarkTechPost ↗Ask about this article