A Survey of Multi-label Text Classification Based on Deep Learning

Chen, Xiaolong; Cheng, Jieren; Liu, Jingxin; Xu, Wenghang; Hua, Shuai; Zhu, Tongyu; Sheng, Victor S.

doi:10.1007/978-3-031-06794-5_36

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…The datasets with hard classification are not best suitable for our problem statement. The hard classification led to the multilabel text classification [66]. Multi-label text classification is a stand-alone problem without consideration of any hierarchical relationship.…”

Section: A Corporamentioning

confidence: 99%

Advanced Hierarchical Topic Labeling for Short Text

et al. 2023

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Hierarchical Topic Modeling is the probabilistic approach for discovering latent topics distributed hierarchically among the documents. The distributed topics are represented with the respective topic terms. An unambiguous conclusion from the topic term distribution is a challenge for readers. The hierarchical topic labeling eases the challenge by facilitating an individual, appropriate label for each topic at every level. In this work, we propose a BERT-embedding inspired methodology for labeling hierarchical topics in short text corpora. The short texts have gained significant popularity on multiple platforms in diverse domains. The limited information available in the short text makes it difficult to deal with. In our work, we have used three diverse short text datasets that include both structured and unstructured instances. Such diversity ensures the broad application scope of this work. Considering the relevancy factor of the labels, the proposed methodology has been compared against both automatic and human annotators. Our proposed methodology outperformed the benchmark with an average score of 0.4185, 49.50, and 49.16 for cosine similarity, exact match, and partial match, respectively.

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Section: A Corporamentioning

confidence: 99%

Advanced Hierarchical Topic Labeling for Short Text

et al. 2023

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“…In the field of multi-label text classification, numerous studies have contributed to the development of effective models and techniques (Jiang et al, 2021;. Previous research has explored a variety of methodologies, including traditional machine learning algorithms, deep learning architectures, and hybrid models, to address the complex nature of multi-label classification tasks (Chen et al, 2022). Notable work has been conducted on feature engineering (Scott and Matwin, 1999;Yao et al, 2018), neural network architectures (Onan, 2022;Soni et al, 2022), and loss functions tailored for multi-label scenarios (Hullermeier et al, 2020), aiming to enhance the predictive accuracy and interpretability of models.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation

Li,

Zhu,

van de Loo

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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Extreme multi-label text classification is a prevalent task in industry, but it frequently encounters challenges in terms of machine learning perspectives, including model limitations, data scarcity, and time-consuming evaluation. This paper aims to mitigate these issues by introducing novel approaches. Firstly, we propose a label ranking model as an alternative to the conventional SciBERT-based classification model, enabling efficient handling of largescale labels and accommodating new labels. Secondly, we present an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. Finally, we introduce ChatGPT to assist with model evaluation. Our experiments demonstrate the effectiveness of these techniques in enhancing the extreme multi-label text classification task.

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“…In the era of big data explosion, text classification, as one of the fundamental tasks in the field of NLP, has received a lot of attention based on the urgent demand of human beings for efficient text information processing techniques. Text classification [1] refers to classifying a given text according to a preset label. This text can be a sentence, a paragraph, or even a document.…”

Section: Introductionmentioning

confidence: 99%

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

Cheng,

Chen,

et al. 2023

CMC

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In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model's grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model's capacity to understand and represent the data. Additionally, to address the intricate task of mining label correlations, we propose a Dual-end enhancement mechanism. This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation. We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets. These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism. Our final model unequivocally outperforms the baseline model, attesting to its robustness. These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.

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A Survey of Multi-label Text Classification Based on Deep Learning

Cited by 9 publications

References 53 publications

Advanced Hierarchical Topic Labeling for Short Text

Advanced Hierarchical Topic Labeling for Short Text

Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

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