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The Classification of Scientific Abstracts Using Text Statistical Features.

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Automated classification of scientific abstracts using text statistical features and machine learning models can improve literature screening. This study found a random forest model with keywords and Word2Vec embeddings achieved the best results, offering a computationally inexpensive approach.

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Area of Science:

  • Bibliometrics
  • Natural Language Processing
  • Machine Learning

Background:

  • Scientific literature screening is crucial but time-consuming.
  • Automated classification of abstracts can enhance efficiency.
  • Statistical properties of text are potential features for classification.

Purpose of the Study:

  • To evaluate the quality of automated medical abstract classification.
  • To assess the effectiveness of text statistical features for this task.
  • To identify optimal machine learning models and features for abstract classification.

Main Methods:

  • Performed twelve experiments using various machine learning models.
  • Utilized text statistical features and three-dimensional Word2Vec embeddings.
  • Tested models on a dataset of 671 medical article abstracts, repeating each experiment 300 times.

Main Results:

  • Achieved the best classification performance with an F1 score of 0.775.
  • The top-performing model was a random forest classifier.
  • Keywords combined with three-dimensional Word2Vec embeddings yielded the best results.

Conclusions:

  • Straightforward and computationally inexpensive methods can effectively classify scientific abstracts.
  • The proposed approach is expected to facilitate literature selection for researchers.
  • Automated abstract classification based on statistical text features is a viable tool for scientific literature screening.