2023/11/21 by Shufan Jiang, Jiang, Shufan, Pierre Senellart +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Mathematics, Computing, and Information Processing #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2311.12448
openalex publication_date 2023/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We consider automatically identifying the defined term within a mathematical definition from the text of an academic article. Inspired by the development of transformer-based natural language processing applications, we pose the problem as (a) a token-level classification task using fine-tuned pre-trained transformers; and (b) a question-answering task using a generalist large language model (GPT). We also propose a rule-based approach to build a labeled dataset from the LATEX source of papers. Experimental results show that it is possible to reach high levels of precision and recall using either recent (and expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.