Results 221 to 230 of about 31,930 (259)

Arabic Multi-label Text Classification of News Articles

2021
Text classification is the process of automatically tagging a textual document with the most relevant set of labels. This work aims to automatically map an input document based on its vocabulary features to multiple tags. To achieve this goal, a large dataset has been constructed from various Arabic news portals.
Hozayfa El Rifai, Leen Al Qadi, Ashraf Elnagar   +2 more
openaire   +1 more source

Hierarchical multi-label classification of social text streams

Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014
Hierarchical multi-label classification assigns a document to multiple hierarchical classes. In this paper we focus on hierarchical multi-label classification of social text streams. Concept drift, complicated relations among classes, and the limited length of documents in social text streams make this a challenging problem. Our approach includes three
Zhaochun Ren, Maria-Hendrike Peetz, Shangsong Liang, Willemijn van Dolen, Maarten de Rijke   +4 more
openaire   +3 more sources

Multi-label text classification with an ensemble feature space

Journal of Intelligent & Fuzzy Systems, 2021
Multi-label text classification aims at assigning more than one class to a given text document, which makes the task more ambiguous and challenging at the same time. The ambiguities come from the fact that often several labels in the prescribed label set are semantically close to each other, making clear demarcation between them difficult.
Kushagri Tandon, Niladri Chatterjee
openaire   +1 more source

BERT for Sequence-to-Sequence Multi-label Text Classification

2021
We study the BERT language representation model and the sequence generation model with BERT encoder for the multi-label text classification task. We show that the Sequence Generating BERT model achieves decent results in significantly fewer training epochs compared to the standard BERT.
Ramil Yarullin, Pavel Serdyukov
openaire   +1 more source

Deep Learning for Extreme Multi-label Text Classification

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017
Extreme multi-label text classification (XMTC) refers to the problem of assigning to each document its most relevant subset of class labels from an extremely large label collection, where the number of labels could reach hundreds of thousands or millions. The huge label space raises research challenges such as data sparsity and scalability. Significant
Jingzhou Liu, Wei-Cheng Chang, Yuexin Wu, Yiming Yang 0002   +3 more
openaire   +1 more source

Effective multi-label active learning for text classification

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers.
Bishan Yang, Jian-Tao Sun, Tengjiao Wang 0003, Zheng Chen 0001   +3 more
openaire   +1 more source

Deep Dependency Network for Multi-label Text Classification

2020
In multi-label text classification tasks, the effective extraction of text features and the use of correlations between labels are the main starting points to improve the performance of the tasks. This paper utilizes the powerful feature representation ability of deep learning, combined with the intuitionistic and easily extensible label correlations ...
Xiaodong Guo, Yang Weng
openaire   +1 more source

On the Value of Head Labels in Multi-Label Text Classification

ACM Transactions on Knowledge Discovery from Data
A formidable challenge in the multi-label text classification (MLTC) context is that the labels often exhibit a long-tailed distribution, which typically prevents deep MLTC models from obtaining satisfactory performance. To alleviate this problem, most existing solutions attempt to improve tail performance by means of sampling or introducing extra ...
Haobo Wang 0001, Cheng Peng 0011, Hede Dong, Lei Feng 0006, Weiwei Liu 0003, Tianlei Hu, Ke Chen 0005, Gang Chen 0001   +7 more
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Hierarchical Multi-label Classification of Text with Capsule Networks

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2019
Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network ...
Rami Aly, Steffen Remus, Chris Biemann
openaire   +1 more source

Multi-label Text Classification Based on Sequence Model

2019
In the multi-label text classification problem, the category labels are frequently related in the semantic space. In order to enhance the classification performance, using the correlation between labels and using the Encoder in the seq2seq model and the Decoder model with the attention mechanism, a multi-label text classification method based on ...
Wenshi Chen, Xinhui Liu, Dongyu Guo, Mingyu Lu   +3 more
openaire   +1 more source