Intent Detection and Entity Extraction from BioMedical Literature

• URL: http://arxiv.org/abs/2404.03598v2
• Date: Mon, 5 Aug 2024 16:01:13 GMT
• Title: Intent Detection and Entity Extraction from BioMedical Literature
• Authors: Ankan Mullick, Mukur Gupta, Pawan Goyal,
• Abstract summary: 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. We show that Supervised Fine Tuned approaches are still relevant and more effective than general-purpose LLMs.
• Score: 14.52164637112797
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering [0.14999444543328289]
  We introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. We employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction.
  arXiv  Detail & Related papers  (2024-10-29T14:45:12Z)
• BioMNER: A Dataset for Biomedical Method Entity Recognition [25.403593761614424]
  We propose a novel dataset for biomedical method entity recognition. We employ an automated BioMethod entity recognition and information retrieval system to assist human annotation. Our empirical findings reveal that the large parameter counts of language models surprisingly inhibit the effective assimilation of entity extraction patterns.
  arXiv  Detail & Related papers  (2024-06-28T16:34:24Z)
• LLMs in Biomedicine: A study on clinical Named Entity Recognition [42.71263594812782]
  Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks. This paper investigates strategies to enhance their performance for the NER task. Our proposed method, DiRAG, can boost the zero-shot F1 score of LLMs for biomedical NER.
  arXiv  Detail & Related papers  (2024-04-10T22:26:26Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Biomedical Language Models are Robust to Sub-optimal Tokenization [30.175714262031253]
  Most modern biomedical language models (LMs) are pre-trained using standard domain-specific tokenizers. We find that pre-training a biomedical LM using a more accurate biomedical tokenizer does not improve the entity representation quality of a language model.
  arXiv  Detail & Related papers  (2023-06-30T13:35:24Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• Discovering Drug-Target Interaction Knowledge from Biomedical Literature [107.98712673387031]
  The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from literature becomes an urgent demand in the industry. We explore the first end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.
  arXiv  Detail & Related papers  (2021-09-27T17:00:14Z)
• Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [73.37262264915739]
  We show that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks.
  arXiv  Detail & Related papers  (2020-07-31T00:04:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.