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Tutorial builds semantic search for math problems from arXiv

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON The article describes a tutorial on building a semantic search engine and classifier using a specific dataset, which falls under research and development. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Tutorial builds semantic search for math problems from arXiv

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The article describes a tutorial on building a semantic search engine and classifier using a specific dataset, which falls under research and development. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. MarkTechPost TIER_1 English(EN) · Sana Hassan ·

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

    <p>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 semant…

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