Relevant bibliographies by topics / Hierarchical Multi-label Text Classification

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Academic literature on the topic 'Hierarchical Multi-label Text Classification'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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

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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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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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Dissertations / Theses on the topic "Hierarchical Multi-label Text Classification"

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Dendamrongvit, Sareewan. "Induction in Hierarchical Multi-label Domains with Focus on Text Categorization." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/542.

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Induction of classifiers from sets of preclassified training examples is one of the most popular machine learning tasks. This dissertation focuses on the techniques needed in the field of automated text categorization. Here, each document can be labeled with more than one class, sometimes with many classes. Moreover, the classes are hierarchically organized, the mutual relations being typically expressed in terms of a generalization tree. Both aspects (multi-label classification and hierarchically organized classes) have so far received inadequate attention. Existing literature work largely as
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Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.

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This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior t
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Razavi, Amir Hossein. "Automatic Text Ontological Representation and Classification via Fundamental to Specific Conceptual Elements (TOR-FUSE)." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23061.

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In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented so
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Wei, Zhihua. "The research on chinese text multi-label classification." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.

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Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in fea
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Burkhardt, Sophie [Verfasser]. "Online Multi-label Text Classification using Topic Models / Sophie Burkhardt." Mainz : Universitätsbibliothek Mainz, 2018. http://d-nb.info/1173911235/34.

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Sendur, Zeynel. "Text Document Categorization by Machine Learning." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/209.

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Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where pre-defined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we conce
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Li, Xin. "Multi-label Learning under Different Labeling Scenarios." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/350482.

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Computer and Information Science<br>Ph.D.<br>Traditional multi-class classification problems assume that each instance is associated with a single label from category set Y where |Y| > 2. Multi-label classification generalizes multi-class classification by allowing each instance to be associated with multiple labels from Y. In many real world data analysis problems, data objects can be assigned into multiple categories and hence produce multi-label classification problems. For example, an image for object categorization can be labeled as 'desk' and 'chair' simultaneously if it contains both ob
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Artmann, Daniel. "Applying machine learning algorithms to multi-label text classification on GitHub issues." Thesis, Högskolan i Halmstad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43097.

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This report compares five machine learning algorithms in their ability to categorize code repositories. The focus of expanding software projects tend to shift from developing new software to the maintenance of the projects. Maintainers can label code repositories to organize the project, but this requires manual labor and time. This report will evaluate how machine learning algorithms perform in automatically classifying code repositories. Automatic classification can aid the management process by reducing both manual labor and human errors. GitHub provides online hosting for both private and
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Průša, Petr. "Multi-label klasifikace textových dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412872.

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The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
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Rios, Anthony. "Deep Neural Networks for Multi-Label Text Classification: Application to Coding Electronic Medical Records." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/71.

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Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders. The main difficulty wit
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Book chapters on the topic "Hierarchical Multi-label Text Classification"

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Lu, Junyu, Hao Zhang, Zhexu Shen, et al. "Multi-task Hierarchical Cross-Attention Network for Multi-label Text Classification." In Natural Language Processing and Chinese Computing. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17189-5_13.

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Kumar, Ashish, and Durga Toshniwal. "Modeling Text-Label Alignment for Hierarchical Text Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70365-2_10.

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Zhao, Xiuhao, Zhao Li, Xianming Zhang, et al. "An Interactive Fusion Model for Hierarchical Multi-label Text Classification." In Natural Language Processing and Chinese Computing. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-17189-5_14.

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Zhao, Rui, Xiao Wei, Cong Ding, and Yongqi Chen. "Hierarchical Multi-label Text Classification: Self-adaption Semantic Awareness Network Integrating Text Topic and Label Level Information." In Knowledge Science, Engineering and Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82147-0_33.

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Torba, Fatos, Christophe Gravier, Charlotte Laclau, Abderrhammen Kammoun, and Julien Subercaze. "Decoding the Hierarchy: A Hybrid Approach to Hierarchical Multi-label Text Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88708-6_26.

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du Toit, Jaco, and Marcel Dunaiski. "Hierarchical Text Classification Using Language Models with Global Label-Wise Attention Mechanisms." In Artificial Intelligence Research. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49002-6_18.

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Ma, Yinglong, Jingpeng Zhao, and Beihong Jin. "A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories for Hierarchical Multi-label Text Classification." In Artificial Neural Networks and Machine Learning – ICANN 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61616-8_60.

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Slavkov, Ivica, Jana Karcheska, Dragi Kocev, Slobodan Kalajdziski, and Sašo Džeroski. "ReliefF for Hierarchical Multi-label Classification." In New Frontiers in Mining Complex Patterns. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_10.

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Alaydie, Noor, Chandan K. Reddy, and Farshad Fotouhi. "Exploiting Label Dependency for Hierarchical Multi-label Classification." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_25.

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Ananpiriyakul, Thanawut, Piyapan Poomsirivilai, and Peerapon Vateekul. "Label Correction Strategy on Hierarchical Multi-Label Classification." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_17.

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Conference papers on the topic "Hierarchical Multi-label Text Classification"

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Gong, Yujie, Jian Zhang, Yu Lin, and Hongwei Wang. "Optimizing Multi-Class Text Classification with Hierarchical Label Filtering and Label Order Analysis." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033332.

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Kumar, Ashish, and Durga Toshniwal. "Local Hierarchy-Aware Text-Label Association for Hierarchical Text Classification." In 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2024. http://dx.doi.org/10.1109/dsaa61799.2024.10722840.

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Paletto, Lorenzo, Valerio Basile, and Roberto Esposito. "Label Augmentation for Zero-Shot Hierarchical Text Classification." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.416.

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Li, Renxuan Albert, Ihab Hajjar, Felicia Goldstein, and Jinho D. Choi. "Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP Dataset for Early Detection of Alzheimer’s Disease." In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.aacl-main.38.

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Yelamanchili, Likhitha, Ching-Seh Mike Wu, Chris Pollett, and Robert Chun. "Multi-Label Text Classification with Transfer Learning." In 2024 IEEE/ACIS 9th International Conference on Big Data, Cloud Computing, and Data Science (BCD). IEEE, 2024. http://dx.doi.org/10.1109/bcd61269.2024.10743077.

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Liu, Xiaoyun, Weijuan Zhang, Kun Ma, Yanfang Qiu, Ke Ji, and Bo Yang. "LMFN: Label-Aware Multi-Semantic Fusion Network for Multi-Label Text Classification." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033556.

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Peng, Lim Guan, and Wandeep Kaur Ratan Singh. "Multi Label Clinical Text Classification Using Attention Mechanism." In 2024 16th International Conference on Knowledge and System Engineering (KSE). IEEE, 2024. https://doi.org/10.1109/kse63888.2024.11063651.

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Gu, Qiliang, Shuo Zhao, Jianqiang Zhang, Gongpeng Song, and Qin Lu. "MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831836.

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Xu, Pengyu, Liping Jing, and Jian Yu. "Enhancing Multi-Label Text Classification under Label-Dependent Noise: A Label-Specific Denoising Framework." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.324.

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Huang, Wei, Enhong Chen, Qi Liu, et al. "Hierarchical Multi-label Text Classification." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. ACM, 2019. http://dx.doi.org/10.1145/3357384.3357885.

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