Results 31 to 40 of about 31,930 (259)

Hierarchical Transfer Learning for Multi-label Text Classification [PDF]

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019
Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category. We propose a novel transfer learning based strategy, HTrans, where binary
Siddhartha Banerjee, Cem Akkaya, Francisco Perez-Sorrosal, Kostas Tsioutsiouliklis   +3 more
openaire   +1 more source

MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS

Jordanian Journal of Computers and Information Technology, 2020
Multi-label classification is a general type of classification that has attracted many researchers in the last two decades due to its applicability to many modern domains, such as scene classification, bioinformatics and text classification, among others.
Raed Alazaidah, Farzana Kabir Ahmad, Mohamad Farhan Mohamad Mohsin, Wael Ahmad AlZoubi   +3 more
doaj   +1 more source

Multi-Label Classification of Chinese Rural Poverty Governance Texts Based on XLNet and Bi-LSTM Fused Hierarchical Attention Mechanism

Applied Sciences, 2023
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.
Xin Wang, Leifeng Guo
doaj   +1 more source

SemFA:Extreme Multi-label Text Classification Model Based on Semantic Features and Association Attention [PDF]

Jisuanji kexue, 2023
Extreme multi-label text classification(XMTC) is a challenging task that involves finding the most relevant labels from a large and complex label set for a given text sample.Currently,deep learning methods based on the Transformer model have achieved ...
WANG Zhendong, DONG Kaikun, HUANG Junheng, WANG Bailing
doaj   +1 more source

Study on Short Text Classification with Imperfect Labels [PDF]

Jisuanji kexue, 2023
Short text classification techniques have been widely studied.When these techniques are applied to domain short text forproduction,as textual data accumulates,people often encounter problems mainly in two aspects:the imperfect labels and mistakenly ...
LIANG Haowei, WANG Shi, CAO Cungen
doaj   +1 more source

A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification

IEEE Access, 2020
The multi-label text classification task aims to tag a document with a series of labels. Previous studies usually treated labels as symbols without semantics and ignored the relation among labels, which caused information loss.
Linkun Cai, Yu Song, Tao Liu, Kunli Zhang   +3 more
doaj   +1 more source

MLGN:A Multi-Label Guided Network for Improving Text Classification

IEEE Access, 2023
Within natural language processing, multi-label classification is an important but challenging task. It is more complex than single-label classification since the document representations need to cover fine-grained label information, while the labels ...
Qiang Liu, Jingzhe Chen, Fan Chen, Kejie Fang, Peng An, Yiming Zhang, Shiyu Du   +6 more
doaj   +1 more source

A Multi-Label Text Categorization Algorithm Incorporating Label-Guided Attention Mechanisms

IEEE Access
This paper proposes a multi-label text classification algorithm based on causal relationships to address the current challenge of accurately capturing label correlations in multi-label text classification tasks. The algorithm comprises a basic prediction
Shaocong Guo, Qian Hao
doaj   +1 more source

Multi-label Text Classification by Fusing Pseudo-label Generation and Data Augmentation

Journal of Harbin University of Science and Technology
Aiming at the problem that the multi-label text classification algorithm ignores the noise label and lacks the combination incentive of true and false, which leads to the weak robustness of the model and the poor classification effect, a cascaded BiLSTM-
WANG Shuitao, ZHANG Sijia, SUN Xufei, YANG Jingjie, BI Tiantian, ZHANG Zhenglong   +5 more
doaj   +1 more source

Generative Multi-Task Learning for Text Classification

IEEE Access, 2020
Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which ...
Wei Zhao, Hui Gao, Shuhui Chen, Nan Wang
doaj   +1 more source