@inproceedings{liu-etal-2019-neuralclassifier,
title = "{N}eural{C}lassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit",
author = "Liu, Liqun and
Mu, Funan and
Li, Pengyu and
Mu, Xin and
Tang, Jing and
Ai, Xingsheng and
Fu, Ran and
Wang, Lifeng and
Zhou, Xing",
editor = "Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-3015",
doi = "10.18653/v1/P19-3015",
pages = "87--92",
abstract = "In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.",
}
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<abstract>In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.</abstract>
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%0 Conference Proceedings
%T NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
%A Liu, Liqun
%A Mu, Funan
%A Li, Pengyu
%A Mu, Xin
%A Tang, Jing
%A Ai, Xingsheng
%A Fu, Ran
%A Wang, Lifeng
%A Zhou, Xing
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-neuralclassifier
%X In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.
%R 10.18653/v1/P19-3015
%U https://aclanthology.org/P19-3015
%U https://doi.org/10.18653/v1/P19-3015
%P 87-92
Markdown (Informal)
[NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit](https://aclanthology.org/P19-3015) (Liu et al., ACL 2019)
ACL
- Liqun Liu, Funan Mu, Pengyu Li, Xin Mu, Jing Tang, Xingsheng Ai, Ran Fu, Lifeng Wang, and Xing Zhou. 2019. NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 87–92, Florence, Italy. Association for Computational Linguistics.