The overall purpose of this paper is to demonstrate how data preprocessing, training size
variation, and subsampling can dynamically change the performance metrics of imbalanced text
classification. The methodology encompasses using two different supervised learning classification
approaches of feature engineering and data preprocessing with the use of five machine learning
classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes,
statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided
into five labels from the World Health Organization Coronavirus Research Downloadable Articles of
COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification
that affects the performance metrics of precision, recall, receiver operating characteristic area under
the curve, and accuracy. One approach that involves labeling rows of sentences based on regular
expressions significantly improved the performance of imbalanced sampling techniques verified by
performing statistical analysis using a t-test documenting performance metrics of iterations versus
another approach that automatically labels the sentences based on how the documents are organized
into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and
sampling techniques in text classification datasets, with different performance levels and class
imbalance issues observed in manual and automatic methods of data processing.
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Research Replication Prediction Using Weakly Supervised Learning
Knowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning aided methods to predict scientific claims’ replicability. However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers’ text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles. Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques. Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data. In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset. Further, we are able to achieve an accuracy of 75.76% for predicting the replicability of research.
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- Award ID(s):
- 2007951
- NSF-PAR ID:
- 10282449
- Date Published:
- Journal Name:
- In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings
- Page Range / eLocation ID:
- 1464 to 1474
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2020.findings-emnlp.132
