Ensemble application of convolutional and recurrent neural networks for multi-label text categorization
By Guibin Chen, Deheng Ye, Zhenchang Xing, Jieshan Chen, Erik Cambria. 2017
245 citations recorded by the source corpus.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Erik Cambria
- Soujanya Poria
- Devamanyu Hazarika
- Tom Young
- Percy Liang
- Louis-philippe Morency
- Navonil Majumder
- Shervin Minaee
Citations in the corpus
- Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “More specific use cases include applications such as document classification (Chaturvedi et al., 2016), multi-label text categorization (Chen et al., 2017), multimodal sentiment analysis (Poria et al., 2017; Zadeh et al., 2017; Tong et al., 2017), and subjectivity detection (Chaturvedi et al.,…”
- Ensemble deep learning: A review (2021, methodology): “[201] propose an ensemble application of convolutional and recurrent neural networks to capture both the global and the local textual semantics and to model high-order label correlations.”
- Deep Learning--based Text Classification (2020, background): “[130] perform multi-label text categorization through a CNN-RNN model that is able to capture both global and local textual semantics and, hence, to model high-order label correlations while having a tractable computational complexity.”
- A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “In Chen et al. (2017) presented voting based on the CNN_RNN model using a large documents dataset (Lewis et al., 2004).”
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “More specific use cases include applications such as multilabel text categorization [76], multimodal sentiment analysis [77]–[79], and subjectivity detection [80].”
- SGM: Sequence Generation Model for Multi-label Classification (2018, methodology): “In addition, training strategies mentioned in Zhang and Wallace (2015) are used to tune hyper-parameters for the baselines CNN and CNN-RNN.”