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Journal articles on the topic 'Hierarchical Multi-label Text Classification'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

林, 娜. "Hierarchical Multi-label Text Classification Based on Bert." Advances in Applied Mathematics 13, no. 05 (2024): 2141–47. http://dx.doi.org/10.12677/aam.2024.135202.

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Ma, Yinglong, Xiaofeng Liu, Lijiao Zhao, Yue Liang, Peng Zhang, and Beihong Jin. "Hybrid embedding-based text representation for hierarchical multi-label text classification." Expert Systems with Applications 187 (January 2022): 115905. http://dx.doi.org/10.1016/j.eswa.2021.115905.

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3

Giulia Ferraro and Luca Benedetti. "Hierarchical Multi-Task Learning for Fine-Grained and Coarse Text Classification." Frontiers in Interdisciplinary Applied Science 2, no. 2 (2025): 184–90. https://doi.org/10.71465/fias272.

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Text classification tasks often vary in granularity, with coarse labels capturing general topics and fine-grained labels capturing nuanced subcategories or sentiments. Traditional models trained separately on these classification levels struggle to leverage the hierarchical relationships between them. In this paper, we propose a hierarchical multi-task learning (HMTL) framework that jointly models coarse and fine-grained text classification tasks by aligning shared and task-specific layers in a hierarchical architecture. Our model exploits the inherent semantic dependencies between classificat

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Manoharan J, Samuel. "Capsule Network Algorithm for Performance Optimization of Text Classification." March 2021 3, no. 1 (2021): 1–9. http://dx.doi.org/10.36548/jscp.2021.1.001.

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In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent dat

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Zhang, Ke, Yufei Tu, Jun Lu, et al. "Multi-Head Hierarchical Attention Framework with Multi-Level Learning Optimization Strategy for Legal Text Recognition." Electronics 14, no. 10 (2025): 1946. https://doi.org/10.3390/electronics14101946.

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Owing to the rapid increase in the amount of legal text data and the increasing demand for intelligent processing, multi-label legal text recognition is becoming increasingly important in practical applications such as legal information retrieval and case classification. However, traditional methods have limitations in handling the complex semantics and multi-label characteristics of legal texts, making it difficult to accurately extract feature and effective category information. Therefore, this study proposes a novel multi-head hierarchical attention framework suitable for multi-label legal

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Wang, Boyan, Xuegang Hu, Peipei Li, and Philip S. Yu. "Cognitive structure learning model for hierarchical multi-label text classification." Knowledge-Based Systems 218 (April 2021): 106876. http://dx.doi.org/10.1016/j.knosys.2021.106876.

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Gargiulo, Francesco, Stefano Silvestri, Mario Ciampi, and Giuseppe De Pietro. "Deep neural network for hierarchical extreme multi-label text classification." Applied Soft Computing 79 (June 2019): 125–38. http://dx.doi.org/10.1016/j.asoc.2019.03.041.

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Yang, Zhenyu, and Guojing Liu. "Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification." IEEE Access 7 (2019): 153012–20. http://dx.doi.org/10.1109/access.2019.2948855.

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Wang, Xin, and Leifeng Guo. "Multi-Label Classification of Chinese Rural Poverty Governance Texts Based on XLNet and Bi-LSTM Fused Hierarchical Attention Mechanism." Applied Sciences 13, no. 13 (2023): 7377. http://dx.doi.org/10.3390/app13137377.

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Hierarchical multi-label text classification (HMTC) is a highly relevant and widely discussed topic in the era of big data, particularly for efficiently classifying extensive amounts of text data. This study proposes the HTMC-PGT framework for poverty governance’s single-path hierarchical multi-label classification problem. The framework simplifies the HMTC problem into training and combination problems of multi-class classifiers in the classifier tree. Each independent classifier in this framework uses an XLNet pretrained model to extract char-level semantic embeddings of text and employs a h

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Zhang, Xinyi, Jiahao Xu, Charlie Soh, and Lihui Chen. "LA-HCN: Label-based Attention for Hierarchical Multi-label Text Classification Neural Network." Expert Systems with Applications 187 (January 2022): 115922. http://dx.doi.org/10.1016/j.eswa.2021.115922.

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Zhu, He, Jinxiang Xia, Ruomei Liu, and Bowen Deng. "SPIRIT: Structural Entropy Guided Prefix Tuning for Hierarchical Text Classification." Entropy 27, no. 2 (2025): 128. https://doi.org/10.3390/e27020128.

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Hierarchical text classification (HTC) is a challenging task that requires classifiers to solve a series of multi-label subtasks considering hierarchical dependencies among labels. Recent studies have introduced prompt tuning to create closer connections between the language model (LM) and the complex label hierarchy. However, we find that the model’s attention to the prompt gradually decreases as the prompt moves from the input to the output layer, revealing the limitations of previous prompt tuning methods for HTC. Given the success of prefix tuning-based studies in natural language understa

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Lin, Sanne, Flavius Frasincar, and Jasmijn Klinkhamer. "Hierarchical deep learning for multi-label imbalanced text classification of economic literature." Applied Soft Computing 176 (May 2025): 113189. https://doi.org/10.1016/j.asoc.2025.113189.

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13

Vogrincic, Sergeja, and Zoran Bosnic. "Ontology-based multi-label classification of economic articles." Computer Science and Information Systems 8, no. 1 (2011): 101–19. http://dx.doi.org/10.2298/csis100420034v.

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The paper presents an approach to the task of automatic document categorization in the field of economics. Since the documents can be annotated with multiple keywords (labels), we approach this task by applying and evaluating multi-label classification methods of supervised machine learning. We describe forming a test corpus of 1015 economic documents that we automatically classify using a tool which integrates ontology construction with text mining methods. In our experimental work, we evaluate three groups of multi-label classification approaches: transformation to single-class problems, spe

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Chen, Huiyao, Yu Zhao, Zulong Chen, et al. "Retrieval-style In-context Learning for Few-shot Hierarchical Text Classification." Transactions of the Association for Computational Linguistics 12 (2024): 1214–31. http://dx.doi.org/10.1162/tacl_a_00697.

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Abstract Hierarchical text classification (HTC) is an important task with broad applications, and few-shot HTC has gained increasing interest recently. While in-context learning (ICL) with large language models (LLMs) has achieved significant success in few-shot learning, it is not as effective for HTC because of the expansive hierarchical label sets and extremely ambiguous labels. In this work, we introduce the first ICL-based framework with LLM for few-shot HTC. We exploit a retrieval database to identify relevant demonstrations, and an iterative policy to manage multi-layer hierarchical lab

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Cheng, Quan, and Wenwan Shi. "Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network." Information Processing & Management 62, no. 1 (2025): 103952. http://dx.doi.org/10.1016/j.ipm.2024.103952.

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Zhao, Fei, Zhen Wu, Liang He, and Xin-Yu Dai. "Label-Correction Capsule Network for Hierarchical Text Classification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2023): 2158–68. http://dx.doi.org/10.1109/taslp.2023.3282099.

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Zeng, Biqing, Yihao Peng, Jichen Yang, Peilin Hong, and Junjie Liang. "CPM-based Hierarchical Text Classification." Journal of Artificial Intelligence Research 82 (January 28, 2025): 367–88. https://doi.org/10.1613/jair.1.16943.

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In the field of natural language processing, hierarchical text classification (HTC) has emerged as a critical task for organizing and analyzing large volumes of text data. The previous work of HTC often falls short in fully leveraging the hierarchical structure of labels, resulting in suboptimal performance. In addition, it is difficult to capture nuanced relationships between parent and child classes, leading to inaccurate predictions and insufficient differentiation between sibling classes under the same parent category. This gap underscores the need for approaches that can more effectively

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Gong, Jibing, Hongyuan Ma, Zhiyong Teng, et al. "Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification." IEEE Access 8 (2020): 30885–96. http://dx.doi.org/10.1109/access.2020.2972751.

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Xuan, Zhaoxin, Hejing Zhao, Xin Li, and Ziqi Chen. "Distantly Supervised Relation Extraction Method Based on Multi-Level Hierarchical Attention." Information 16, no. 5 (2025): 364. https://doi.org/10.3390/info16050364.

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Distantly Supervised Relation Extraction (DSRE) aims to automatically identify semantic relationships within large text corpora by aligning with external knowledge bases. Despite the success of current methods in automating data annotation, they introduce two main challenges: label noise and data long-tail distribution. Label noise results in inaccurate annotations, which can undermine the quality of relation extraction. The long-tail problem, on the other hand, leads to an imbalanced model that struggles to extract less frequent, long-tail relations. In this paper, we introduce a novel relati

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Sohrab, Mohammad Golam, Makoto Miwa, and Yutaka Sasaki. "IN-DEDUCTIVE and DAG-Tree Approaches for Large-Scale Extreme Multi-label Hierarchical Text Classification." Polibits 54 (July 31, 2016): 61–70. http://dx.doi.org/10.17562/pb-54-8.

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21

Zeng, Wenhua, Wenhu Tang, Diping Yuan, Hui Zhang, Pinsheng Duan, and Shikun Hu. "Structure-Aware and Format-Enhanced Transformer for Accident Report Modeling." Applied Sciences 15, no. 14 (2025): 7928. https://doi.org/10.3390/app15147928.

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Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, this study proposes SAFE-Transformer, a Structure-Aware and Format-Enhanced Transformer designed for long-document modeling in the emergency safety context. SAFE-Transformer adopts a dual-stream encoding archite

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Yang, Shengtao. "Application of Capsule Network in Text Classification Problem." Applied and Computational Engineering 109, no. 1 (2024): 113–20. http://dx.doi.org/10.54254/2755-2721/109/20241307.

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Abstract. The rise of the Internet and the big data era has led to a significant increase in text data, making text classification an essential task in natural language processing. Conventional deep learning models have advanced considerably in understanding text context dependencies; however, they still struggle with long-range dependencies and multi-scale features. In recent years, capsule networks (CapsNets) have demonstrated outstanding results in the area of image recognition due to their unique structure and dynamic routing mechanism, which has inspired researchers to explore their appli

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Im, SangHun, GiBaeg Kim, Heung-Seon Oh, Seongung Jo, and Dong Hwan Kim. "Hierarchical Text Classification as Sub-hierarchy Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12933–41. http://dx.doi.org/10.1609/aaai.v37i11.26520.

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Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarchical taxonomy. Recent HTC models based on deep learning have attempted to incorporate hierarchy information into a model structure. Consequently, these models are challenging to implement when the model parameters increase for a large-scale hierarchy because the model structure depends on the hierarchy size. To solve this problem, we formulate HTC as a sub-hierarchy sequence ge

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Li, Xiyao, Jiayi Li, Jie Jiang, Xiaofeng Pan, and Xin Huang. "Spatio-temporal-text fusion for hierarchical multi-label crop classification based on time-series remote sensing imagery." International Journal of Applied Earth Observation and Geoinformation 139 (May 2025): 104471. https://doi.org/10.1016/j.jag.2025.104471.

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25

Silvestri, Stefano, Francesco Gargiulo, and Mario Ciampi. "Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network." Applied Sciences 13, no. 24 (2023): 13117. http://dx.doi.org/10.3390/app132413117.

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This paper proposes an innovative method that exploits a complex deep learning network architecture, called Hierarchical Deep Neural Network (HDNN), specifically developed for the eXtreme Multilabel Text Classification (XMTC) task, when the label set is hierarchically organized, such as the case of the PubMed article labeling task. In detail, the topology of the proposed HDNN architecture follows the exact hierarchical structure of the label set to integrate this knowledge directly into the DNN. We assumed that if a label set hierarchy is available, as in the case of the PubMed Dataset, forcin

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Liu, Hankai, Xianying Huang, and Xiaoyang Liu. "Improve label embedding quality through global sensitive GAT for hierarchical text classification." Expert Systems with Applications 238 (March 2024): 122267. http://dx.doi.org/10.1016/j.eswa.2023.122267.

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Qu, Ping, Beibei Zhang, Jiawei Wu, and Hao Yan. "Comparison of Text Classification Algorithms based on Deep Learning." Journal of Computer Technology and Applied Mathematics 1, no. 2 (2024): 35–42. https://doi.org/10.5281/zenodo.12601298.

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In the technical battlefield of text classification, extracting key features and solving the sparsity problem play a decisive role in improving the performance of classification results. Euclidean geometric models often distort the processed vectors because they are difficult to deal with complex data structures. This exploration uses hyperbolic space with huge storage potential and hierarchical structure, and proposes an innovative hyperbolic graph-based short text classification technology - L-HGAT, aiming to improve the efficiency of processing concise information. This method combines two

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Deng, Jiawen, and Fuji Ren. "Hierarchical Network with Label Embedding for Contextual Emotion Recognition." Research 2021 (January 6, 2021): 1–9. http://dx.doi.org/10.34133/2021/3067943.

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Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recogniti

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Kadriu, Arbana, Lejla Abazi, and Hyrije Abazi. "Albanian Text Classification: Bag of Words Model and Word Analogies." Business Systems Research Journal 10, no. 1 (2019): 74–87. http://dx.doi.org/10.2478/bsrj-2019-0006.

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Abstract Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news artic

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Liu, Zhenyu, Chaohong Lu, Haiwei Huang, Shengfei Lyu, and Zhenchao Tao. "Hierarchical Multi-Granularity Attention- Based Hybrid Neural Network for Text Classification." IEEE Access 8 (2020): 149362–71. http://dx.doi.org/10.1109/access.2020.3016727.

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Basuki, Setio, Rizky Indrabayu, and Nico Ardia Effendy. "Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques." Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika 10, no. 2 (2025): 104–10. https://doi.org/10.23917/khif.v10i2.3328.

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This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and

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Narushynska, Olga, Maksym Arzubov, and Vasyl Teslyuk. "Construction of hierarchical classification model for product management: Penalized Information Gain considering dynamic weight coefficients." Eastern-European Journal of Enterprise Technologies 1, no. 3 (133) (2025): 17–27. https://doi.org/10.15587/1729-4061.2025.321273.

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The object of this study is the process of constructing hierarchical classifiers for textual data within a defined taxonomy. The task addressed focuses on minimizing cascading errors and enhancing classification consistency across all hierarchy levels, a critical challenge for deep and imbalanced hierarchical structures. The proposed model leverages the Penalized Information Gain (PIG) criterion with dynamically adjusted weight coefficients. A model for hierarchical text classification has been built. It aims to improve classification accuracy and preserve the structural logic of data within m

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Xie, Ying, Zhengning Li, Yibo Yin, Zibu Wei, Guokun Xu, and Yang Luo. "Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification." Journal of Theory and Practice of Engineering Science 4, no. 02 (2024): 15–22. http://dx.doi.org/10.53469/jtpes.2024.04(02).03.

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The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the

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Hamzaoui, Benamar, Djelloul Bouchiha, and Abdelghani Bouziane. "A comprehensive survey on arabic text classification: progress, challenges, and techniques." Brazilian Journal of Technology 8, no. 1 (2025): e77611. https://doi.org/10.38152/bjtv8n1-022.

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The exponential growth of textual data has heightened the importance of efficient text classification, a fundamental natural language processing task that assigns predefined categories to documents. This task can follow flat classification, where categories are equally structured, or hierarchical classification, which organizes categories in multi-level structures and presents additional complexities. While extensive research has advanced text classification for English, studies on Arabic text classification remain limited, particularly in hierarchical contexts. The unique features of Arabic,

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Schiavone, Alice, Lea Marie Pehrson, Silvia Ingala, et al. "Effective Machine Learning Techniques for Non-English Radiology Report Classification: A Danish Case Study." AI 6, no. 2 (2025): 37. https://doi.org/10.3390/ai6020037.

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Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. Previous work in natural language processing has shown that it is possible to automatically extract annotations from the free-text reports associated with chest X-rays. Methods: This study investigated techniques to extract 49 labels in a hierarchical tree structure from chest X-ray reports written in Danish. The labels were extracted from approximately 550,000 reports by performing multi-class, multi-label classification using a method based o

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Huang, Weihao, Jiaojiao Chen, Qianhua Cai, Xuejie Liu, Yudong Zhang, and Xiaohui Hu. "Hierarchical Hybrid Neural Networks With Multi-Head Attention for Document Classification." International Journal of Data Warehousing and Mining 18, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijdwm.303673.

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Document classification is a research topic aiming to predict the overall text sentiment polarity with the advent of deep neural networks. Various deep learning algorithms have been employed in the current studies to improve classification performance. To this end, this paper proposes a hierarchical hybrid neural network with multi-head attention (HHNN-MHA) model on the task of document classification. The proposed model contains two layers to deal with the word-sentence level and sentence-document level classification respectively. In the first layer, CNN is integrated into Bi-GRU and a multi

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La Grassa, Riccardo, Ignazio Gallo, and Nicola Landro. "Learn class hierarchy using convolutional neural networks." Applied Intelligence 51, no. 10 (2021): 6622–32. http://dx.doi.org/10.1007/s10489-020-02103-6.

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AbstractA large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification, introducing a stack of deep linear layers using cross-entropy loss functions combined to a center loss function. The proposed architecture can extend any neural network model and simultaneously optimizes loss f

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Jin, Yetong, Linfu Sun, and Songlin He. "Convergence of polarized self-attention with consistent rank Chinese text classification." Theoretical and Natural Science 31, no. 1 (2024): 76–81. http://dx.doi.org/10.54254/2753-8818/31/20241133.

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Utilizing the powerful feature extraction capabilities of deep learning, a text classification algorithm with multi-dimensional and high-domain adaptability is designed in this study. This method enhances the models understanding of topics and content by incorporating the Polarized Self-Attention (PSA) module, which strengthens the spatial structure and semantic features of textual information. The loss function is redesigned to assign smaller losses to misclassifications of neighboring categories, allowing the model to optimize classification accuracy while learning hierarchical structural in

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Liu, Jing, Xiaoying Wang, Yan Tan, Lihua Huang, and Yue Wang. "An Attention-Based Multi-Representational Fusion Method for Social-Media-Based Text Classification." Information 13, no. 4 (2022): 171. http://dx.doi.org/10.3390/info13040171.

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There exist various text-classification tasks using user-generated contents (UGC) on social media in the big data era. In view of advantages and disadvantages of feature-engineering-based machine-learning models and deep-learning models, we argue that fusing handcrafted-text representation via feature engineering and data-driven deep-text representations extracted by performing deep-learning methods is conducive to enhancing text-classification capability. Given the characteristics of different deep neural networks, their complementary effect needs to be investigated. Moreover, contributions o

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Dhina, M. M., and S. Sumathi. "An innovative approach to classify hierarchical remarks with multi-class using BERT and customized naïve bayes classifier." International Journal of Engineering, Science and Technology 13, no. 4 (2022): 32–45. http://dx.doi.org/10.4314/ijest.v13i4.4.

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Text classification is the process of grouping text into distinct categories. Text classifiers may automatically assess text input and allocate a set of pre-defined tags or categories depending on its content or a pre-trained model using Natural Language Processing (NLP), which actually is a subset of Machine Learning (ML). The notion of text categorization is becoming increasingly essential in enterprises since it helps firms to get ideas from facts and automate company operations, lowering manual labor and expenses. Linguistic Detectors (the technique of determining the language of a given d

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Ado, Abubakar, Abdulkadir Abubakar Bichi, Usman Haruna, et al. "An improved multi-stage framework for large-scale hierarchical text classification problems using a modified feature hashing and bi-filtering strategy." International Journal of Data and Network Science 8, no. 4 (2024): 2193–204. http://dx.doi.org/10.5267/j.ijdns.2024.6.012.

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The classification of large-scale textual dataset is associated with a huge number of instances and millions of features which must be discriminated between large numbers of categories. The task requires the utilization of a defined hierarchy structure and tools that automatically classify instances within the hierarchy known as Large Scale Hierarchical Text Classification (LSHTC). Predicting the labels of instances by the employed classifiers is challenging due to the high number of features. Furthermore, the existing Dimensional Reduction (DR) approaches in cooperation with the LSHTC framewo

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Chhatwal, Gurunameh Singh, and Jiashu Zhao. "Multitask Learning for Authenticity and Authorship Detection." Electronics 14, no. 6 (2025): 1113. https://doi.org/10.3390/electronics14061113.

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Traditionally, detecting misinformation (real vs. fake) and authorship (human vs. AI) have been addressed as separate classification tasks, leaving a critical gap in real-world scenarios where these challenges increasingly overlap. Motivated by this need, we introduce a unified framework—the Shared–Private Synergy Model (SPSM)—that tackles both authenticity and authorship classification under one umbrella. Our approach is tested on a novel multi-label dataset and evaluated through an exhaustive suite of methods, including traditional machine learning, stylometric feature analysis, and pretrain

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Liu, Yonghao, Mengyu Li, Wei Pang, et al. "Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24696–704. https://doi.org/10.1609/aaai.v39i23.34650.

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Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we intr

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Li, Wenkuan, Dongyuan Li, Hongxia Yin, Lindong Zhang, Zhenfang Zhu, and Peiyu Liu. "Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification." Applied Sciences 9, no. 18 (2019): 3717. http://dx.doi.org/10.3390/app9183717.

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Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first pro

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Shu, Tao, Zhiyi Wang, Huading Jia, Wenjin Zhao, Jixian Zhou, and Tao Peng. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China." International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12793. http://dx.doi.org/10.3390/ijerph191912793.

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Online game products have fueled the boom in China’s digital economy. Meanwhile, its public health concerns have sparked discussion among consumers on social media. However, past research has seldom studied the public health topics caused by online games from the perspective of consumer opinions. This paper attempts to identify consumers’ opinions on the health impact of online game products through non-structured text and large-size social media comments. Thus, we designed a natural language processing (NLP) framework based on machine learning, which consists of topic mining, multi-label clas

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Chikhi, Achraf, Seyed Sahand Mohammadi Ziabari, and Jan-Willem van Essen. "A Comparative Study of Traditional, Ensemble and Neural Network-Based Natural Language Processing Algorithms." Journal of Risk and Financial Management 16, no. 7 (2023): 327. http://dx.doi.org/10.3390/jrfm16070327.

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Accurate data analysis is an important part of data-driven financial audits. Given the increased data availability and various systems from which audit files are generated, RCSFI provides a way for standardization on behalf of analysis. This research attempted to automate this hierarchical text classification task in order to save financial auditors time and avoid errors. Several studies have shown that ensemble-based models and neural-network-based natural language processing (NLP) techniques achieved encouraging results for classification problems in various domains. However, there has been

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Bates, Stephen, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael Jordan. "Distribution-free, Risk-controlling Prediction Sets." Journal of the ACM 68, no. 6 (2021): 1–34. http://dx.doi.org/10.1145/3478535.

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While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential settings also requires calibrating and communicating the uncertainty of predictions. To convey instance-wise uncertainty for prediction tasks, we show how to generate set-valued predictions from a black-box predictor that controls the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate

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Kauthale, Ravi. "Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1411–18. http://dx.doi.org/10.22214/ijraset.2021.38982.

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Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The exist

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Zhang, Qun, Shiyang Chen, and Wenhe Liu. "Balanced Knowledge Transfer in MTTL-ClinicalBERT: A Symmetrical Multi-Task Learning Framework for Clinical Text Classification." Symmetry 17, no. 6 (2025): 823. https://doi.org/10.3390/sym17060823.

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Clinical text classification presents significant challenges in healthcare informatics due to inherent asymmetries in domain-specific terminology, knowledge distribution across specialties, and imbalanced data availability. We introduce MTTL-ClinicalBERT, a symmetrical multi-task transfer learning framework that harmonizes knowledge sharing across diverse medical specialties while maintaining balanced performance. Our approach addresses the fundamental problem of symmetry in knowledge transfer through three innovative components: (1) an adaptive knowledge distillation mechanism that creates sy

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Sun, Changjian, Wentao Chen, Zhen Zhang, and Tian Zhang. "A Patent Keyword Extraction Method Based on Corpus Classification." Mathematics 12, no. 7 (2024): 1068. http://dx.doi.org/10.3390/math12071068.

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The keyword extraction of patents is crucial for technicians to master the trends of technology. Traditional keyword extraction approaches only handle short text like title or claims, but ignore the comprehensive meaning of the description. This paper proposes a novel patent keyword extraction method based on corpus classification (PKECC), which simulates the patent understanding methods of human patent examiners. First of all, a corpus classification model based on multi-level attention mechanism adopts the Bert model and hierarchical attention mechanism to classify the sentences of patent de

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