A novel reasoning mechanism for multi-label text classification

•A novel reasoning-based algorithm named ML-Reasoner for multi-label text classification is proposed.•Each instance of reasoning in this method takes the previously predicted likelihoods for all labels as additional input.•Not only is the method able to avoid the dependency of label orders completel...

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Vydáno v:Information processing & management Ročník 58; číslo 2; s. 102441
Hlavní autoři: Wang, Ran, Ridley, Robert, Su, Xi’ao, Qu, Weiguang, Dai, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.03.2021
Elsevier Science Publishers
Elsevier Science Ltd
Témata:
Algorithms
Analysis
Classification
Classifiers
Cognition & reasoning
Descriptive labeling
Experiments
Grammatical aspect
Iterative methods
Iterative reasoning mechanism
Label embedding
Labeling
Labels
Machine learning
Multi-label learning
Novels
Reasoning
Text categorization
Text classification
99-00
Multi-label learning
Label embedding
Iterative reasoning mechanism
00-01
Text classification
ISSN:0306-4573, 1873-5371
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Shrnutí:•A novel reasoning-based algorithm named ML-Reasoner for multi-label text classification is proposed.•Each instance of reasoning in this method takes the previously predicted likelihoods for all labels as additional input.•Not only is the method able to avoid the dependency of label orders completely, but it also achieves competitive performance when handling with multi-label datasets.•Applying the reasoning mechanism to three strong neural-based base models can achieve significant performance improvements on all two data sets.•The method achieves state-of-the-art results on two challenging multi-label datasets. The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set. The performance of these approaches is therefore highly variable depending on the order in which the labels are arranged. To avoid being dependent on label order, we design a reasoning-based algorithm named Multi-Label Reasoner (ML-Reasoner) for multi-label classification. ML-Reasoner employs a binary classifier to predict all labels simultaneously and applies a novel iterative reasoning mechanism to effectively utilize the inter-label information, where each instance of reasoning takes the previously predicted likelihoods for all labels as additional input. This approach is able to utilize information between labels, while avoiding the issue of label-order sensitivity. Extensive experiments demonstrate that our method outperforms state-of-the art approaches on the challenging AAPD dataset. We also apply our reasoning module to a variety of strong neural-based base models and show that it is able to boost performance significantly in each case.
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ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2020.102441