Andrea Ferreira


2025

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Building a Compact Math Corpus
Andrea Ferreira
Proceedings of the 5th Workshop on Natural Logic Meets Machine Learning (NALOMA)

This paper introduces the Compact Math Corpus (CMC), a preliminary resource for natural language processing in the mathematics domain. We process three open-access undergraduate textbooks from distinct mathematical areas and annotate them in the CoNLL-U format using a lightweight pipeline based on the spaCy Small model. The structured output enables the extraction of syntactic bigrams and TF-IDF scores, supporting a syntactic-semantic analysis of mathematical sentences.From the annotated data, we construct a classification dataset comprising bigrams potentially representing mathematical concepts, along with representative example sentences. We combine CMC with the conversational corpus UD English EWT and train a logistic regression model with K-fold cross-validation, achieving a minimum macro-F1 score of 0.989. These results indicate the feasibility of automatic concept identification in mathematical texts.The study is designed for easy replication in low-resource settings and to promote sustainable research practices. Our approach offers a viable path to tasks such as parser adaptation, terminology extraction, multiword expression modeling, and improved analysis of mathematical language structures.
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