In mathematical literature, terms can have multiple meanings based on context. Manual disambiguation across scholarly articles demands massive efforts from mathematicians. This paper addresses the challenge of automatically determining whether two definitions of a mathematical term are semantically different. Specifically, the difficulties and how contextualized textual representation can help resolve the problem, are investigated. A new dataset MathD2 for mathematical term disambiguation is constructed with ProofWiki’s disambiguation pages. Then three approaches based on the contextualized textual representation are studied: (1) supervised classification based on the embedding of concatenated definition and title; (2) zero-shot prediction based on semantic textual similarity(STS) between definition and title and (3) zero-shot LLM prompting. The first two approaches achieve accuracy greater than 0.9 on the ground truth dataset, demonstrating the effectiveness of our methods for the automatic disambiguation of mathematical definitions. Our dataset and source code are available here: https://github.com/sufianj/MathTermDisambiguation.