Intent Detection and Entity Extraction from BioMedical Literature

Ankan Mullick, Mukur Gupta, Pawan Goyal
4/4/2024
cs.CL

No official code found. Best implementation: bioNLU-coling2024/biomed-NER-intent_detection (8 stars, last commit 2024-04-05).

Abstract

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.

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Code Implementations(1)

bioNLU-coling2024/biomed-NER-intent_detectionCommunity100%
83Shell, PythonNov 29, 20232 years ago

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Cite this paper

@article{mullick2024intent,
  title  = {Intent Detection and Entity Extraction from BioMedical Literature},
  author = {Ankan Mullick and Mukur Gupta and Pawan Goyal},
  year   = {2024},
  eprint = {2404.03598},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.03598v2}
}

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