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

news_article.exe📰Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k DatasetBuilding a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset June 4, 2026 2 viewsSource: MarkTechPostIn 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...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. Also, we train a classifier to predict problem status from embeddings and detect closely related or near-duplicate problems. Copy CodeCopiedUse a different Browser We begin by installing the required libraries and importing the tools needed for analysis, visualization, embeddings, and data handling. We also set the main configuration values, including sample size, random seed, and embedding model. This gives us a clean setup before we start working with the ResearchMath dataset. Copy CodeCopiedUse a different Browser We load the amphora/ResearchMath-14k dataset from Hugging Face and convert it into a pandas DataFrame. We inspect the number of rows, available columns, and a few sample records to understand the dataset structure. We then keep only problem statements of meaningful length so that subsequent analysis works on useful text. Copy CodeCopiedUse a different Browser We explore the dataset by checking how problems are distributed across open-status labels and mathematical fields. We visualize the status counts, field counts, and problem lengths to quickly get an overview of the corpus. We also create a heatmap to see how open-status categories vary across different math fields. Copy CodeCopiedUse a different Browser We use TF-IDF to find the most important terms within each top-level mathematical field. We group the dataset by field and extract the strongest keywords or phrases that represent each group. sample the dataset and convert each mathematical problem into a semantic embedding using a SentenceTransformer model. We reduce the embeddings into two dimensions using UMAP, or PCA if UMAP is unavailable, and visualize the problem landscape by field. We then apply K-Means clustering and compare the resulting clusters with the human-labeled taxonomy using ARI and NMI. Copy CodeCopiedUse a different Browser We build a semantic search function that retrieves the most similar research problems for a given query. We then train a classifier on the embeddings to predict each problems open-status label. Finally, we compute similarity across all embedded problems to detect the closest pair and identify near-duplicate or strongly related problem statements. In conclusion, we have a complete workflow for analyzing research-level mathematical problems using modern NLP and machine learning tools. angles. It gives us a practical way to study how mathematical problems are grouped, how similar problems can be retrieved, and how embeddings can support both exploratory analysis and supervised prediction tasks. Check out the Full Codes with Notebook. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset appeared first on MarkTechPost.> Share: Copy link