Intent Detection and Entity Extraction from BioMedical Literature

Autor:	Mullick, Ankan, Gupta, Mukur, Goyal, Pawan
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-language models (LLMs) motivated by endeavours to attain generalized intelligence, their efficacy in replacing task and domain-specific natural language understanding approaches remains questionable. In this paper, we address this question by conducting a comprehensive empirical evaluation of intent detection and named entity recognition (NER) tasks from biomedical text. We show that Supervised Fine Tuned approaches are still relevant and more effective than general-purpose LLMs. Biomedical transformer models such as PubMedBERT can surpass ChatGPT on NER task with only 5 supervised examples. Comment: Accepted to CL4Health LREC-COLING 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.03598 Zobrazit plný text záznamu View this record from Arxiv