Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition

• URL: http://arxiv.org/abs/2412.19346v1
• Date: Thu, 26 Dec 2024 20:24:35 GMT
• Title: Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition
• Authors: Fangyi Chen, Gongbo Zhang, Yilu Fang, Yifan Peng, Chunhua Weng,
• Abstract summary: Existing approaches do not distinguish the attributes of PICO entities.<n>This study aims to develop a named entity recognition model to extract PICO entities with fine granularities.
• Score: 17.791233666137092
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Objective: Extracting PICO elements -- Participants, Intervention, Comparison, and Outcomes -- from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities. Materials and Methods: Using a corpus of 2,511 abstracts with PICO mentions from 4 public datasets, we developed a semi-supervised method to facilitate the training of a NER model, FinePICO, by combining limited annotated data of PICO entities and abundant unlabeled data. For evaluation, we divided the entire dataset into two subsets: a smaller group with annotations and a larger group without annotations. We then established the theoretical lower and upper performance bounds based on the performance of supervised learning models trained solely on the small, annotated subset and on the entire set with complete annotations, respectively. Finally, we evaluated FinePICO on both the smaller annotated subset and the larger, initially unannotated subset. We measured the performance of FinePICO using precision, recall, and F1. Results: Our method achieved precision/recall/F1 of 0.567/0.636/0.60, respectively, using a small set of annotated samples, outperforming the baseline model (F1: 0.437) by more than 16\%. The model demonstrates generalizability to a different PICO framework and to another corpus, which consistently outperforms the benchmark in diverse experimental settings (p-value \textless0.001). Conclusion: This study contributes a generalizable and effective semi-supervised approach to named entity recognition leveraging large unlabeled data together with small, annotated data. It also initially supports fine-grained PICO extraction.

Related papers

• Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary works [2.624902795082451]
  Authorship attribution (AA) tasks rely on statistical data analysis and classification based on stylistic features extracted from texts. In this study, we aimed to significantly improve performance using an integrated integrative ensemble of traditional feature-based and modern PLM-based methods on an AA task in a small sample.
  arXiv  Detail & Related papers  (2025-04-11T13:40:50Z)
• Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness [53.96714099151378]
  We propose a three-step approach for parameter-efficient fine-tuning of image-text foundation models. Our method improves its two key components: minority samples identification and the robust training algorithm. Our theoretical analysis shows that our PPA enhances minority group identification and is Bayes optimal for minimizing the balanced group error.
  arXiv  Detail & Related papers  (2025-03-12T15:46:12Z)
• DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations [0.10485739694839666]
  We constructed DiMB-RE, a comprehensive corpus annotated with diet-microbiome associations. We fine-tuned and evaluated state-of-the-art NLP models for named entity, trigger, and relation extraction.
  arXiv  Detail & Related papers  (2024-09-29T06:58:26Z)
• Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach [0.0]
  sparsity of labelled data is an obstacle to the development of Relation Extraction models. We create the first curated evaluation dataset and extracted literature items from the LOTUS database to build training sets. We evaluate the performance of standard fine-tuning as a generative task and few-shot learning with open Large Language Models.
  arXiv  Detail & Related papers  (2023-11-10T19:36:00Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)
• COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training [15.639976408273784]
  Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. It is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training.
  arXiv  Detail & Related papers  (2023-04-27T08:55:34Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• CEREAL: Few-Sample Clustering Evaluation [4.569028973407756]
  We focus on the underexplored problem of estimating clustering quality with limited labels. We introduce CEREAL, a comprehensive framework for few-sample clustering evaluation. Our results show that CEREAL reduces the area under the absolute error curve by up to 57% compared to the best sampling baseline.
  arXiv  Detail & Related papers  (2022-09-30T19:52:41Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Semi-Automatic Data Annotation guided by Feature Space Projection [117.9296191012968]
  We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.
  arXiv  Detail & Related papers  (2020-07-27T17:03:50Z)
• Learning to Count in the Crowd from Limited Labeled Data [109.2954525909007]
  We focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data.
  arXiv  Detail & Related papers  (2020-07-07T04:17:01Z)

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.