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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Video

Updated: Jul 27, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning.

Rohan Goli1, Keerthana Komatineni1, Shailesh Alluri1

  • 1School of Computing, College of Engineering, Computing and Applied Science, Clemson University, Clemson, SC, USA.

Medrxiv : the Preprint Server for Health Sciences
|June 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a semi-supervised framework for identifying keyphrases (KP) in clinical decision support systems (CDSS) using minimal labeled data. The novel approach enhances interoperability in health information technology by automating ontology construction.

Keywords:
Clinical Decision Support SystemDomain adaptationHierarchical contextMinimal labeled dataNatural language processingSemi-supervised learning

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Area of Science:

  • Health Information Technology
  • Natural Language Processing
  • Machine Learning

Background:

  • Interoperable clinical decision support system (CDSS) rules are crucial for health information technology but achieving interoperability is challenging.
  • Ontology construction, essential for interoperable CDSS rules, traditionally relies on manual keyphrase (KP) identification by domain experts.
  • Natural language processing (NLP) techniques can automate KP identification, complementing manual efforts, but require human expertise for data labeling.

Purpose of the Study:

  • To present a semi-supervised keyphrase identification framework for the CDSS sub-domain.
  • To address the challenge of limited human-labeled data in clinical NLP tasks.
  • To improve the efficiency and accuracy of ontology construction for interoperable CDSS rules.

Main Methods:

  • Developed a semi-supervised framework using BiLSTM-CRF models with hierarchical attention and domain adaptation.
  • Utilized synthetic labels for initial training and fine-tuned with minimal human-labeled data.
  • Optimized the NLP preprocessing and machine learning pipeline, evaluating different encoding schemas and contextual learning strategies.

Main Results:

  • The proposed framework outperforms prior neural architectures by effectively leveraging synthetic labels and document-level context.
  • Domain adaptation techniques improved synthetic label quality, and BIO encoding schema showed slightly better performance.
  • Incorporating document-level context, pre-trained language models, and word embeddings significantly enhanced model performance.

Conclusions:

  • This is the first functional framework for KP identification in the CDSS sub-domain trained on limited human-labeled data.
  • The framework contributes to clinical NLP by offering a light-weighted deep learning approach for real-time KP identification.
  • This automated approach complements human experts, addressing the challenges of manual data labeling in specialized domains.