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| Automatic Noise Reduction of Scientific Domain Document Sets Using Positive-Unlabeled Learning |
| Chen Guo, Yang Zeyu, Chen Jing, Shao Yu |
| Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract In the domain analysis of science and technology, a considerable proportion of unrelated literature (impurities) exists in the datasets constructed by mainstream methods, which weakens the reliability of the final analysis results. Therefore, noise reduction is essential for removing these impurities. Performing automatic noise reduction on a dataset of domain documents without manual annotation is a prerequisite condition for whether the noise-reduction scheme can be universally applied in practice at a low cost. This study aims to transform the noise reduction task into a classification instead of a clustering problem on the premise of making full use of the characteristics of the original document dataset. We introduce positive-unlabeled (PU) learning, which can be conducted using a group of “absolutely positive samples” available in the domain dataset, to obtain reliable negative samples for the final classifiers to fit. Experiments were conducted on a dataset of journals in the MAG online library in the fields of artificial intelligence, economics, and immunology to not only compare the performance of different schemes but also construct two benchmarks and introduce Normalized Discounted Cumulative Gain as an evaluation metric, which proved the effectiveness of our method from the aspects of noise reduction revenue, usability of the result, and effectiveness of document denoising in the context of scientific and technological information analysis.
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Received: 18 December 2023
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