BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task

• URL: http://arxiv.org/abs/2202.13174v1
• Date: Sat, 26 Feb 2022 16:14:27 GMT
• Title: BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
• Authors: Maria Mahbub, Sudarshan Srinivasan, Edmon Begoli and Gregory D Peterson
• Abstract summary: We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task. BioADAPT-MRC is a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets.
• Score: 4.837365865245979
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Motivation: Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. Results: We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability: BioADAPT-MRC is freely available as an open-source project at\\https://github.com/mmahbub/BioADAPT-MRC

Related papers

• 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)
• Augmenting Biomedical Named Entity Recognition with General-domain Resources [47.24727904076347]
  Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. We propose GERBERA, a simple-yet-effective method that utilized a general-domain NER dataset for training. We systematically evaluated GERBERA on five datasets of eight entity types, collectively consisting of 81,410 instances.
  arXiv  Detail & Related papers  (2024-06-15T15:28:02Z)
• BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers [48.21255861863282]
  BMRetriever is a series of dense retrievers for enhancing biomedical retrieval. BMRetriever exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger.
  arXiv  Detail & Related papers  (2024-04-29T05:40:08Z)
• 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)
• Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling [3.8599767910528917]
  This paper proposes a hybrid approach that integrates the strengths of multiple models. BERT provides contextualized word embeddings, a pre-trained multi-channel CNN for character-level information capture, and following by a BiLSTM + CRF for sequence labelling and modelling dependencies between the words in the text. We evaluate our model on the benchmark i2b2/2010 dataset, achieving an F1-score of 90.11.
  arXiv  Detail & Related papers  (2023-12-24T21:45:36Z)
• 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)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition [9.05154470433578]
  Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models. We propose biomedical ALBERT, an effective domain-specific language model trained on large-scale biomedical corpora.
  arXiv  Detail & Related papers  (2020-09-19T12:58:47Z)

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.