The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Recurrent Convolutional Neural Networks for Text Classification
Open accessAbstract
Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.
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Cites
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572
Learning Accurate, Compact, and Interpretable Tree Annotation
Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 · doi:10.3115/1220175.1220230 · Cited by 793
Cited by
TabNet: Attentive Interpretable Tabular Learning
Arik, S. Ö., & Pfister, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16826 · Cited by 1,771
Object detection using YOLO: challenges, architectural successors, datasets and applications
Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13644-y · Cited by 1,549
“…[37], character recognition [83], intrusion detection [32], automatic landslide detection [48], time series prediction [8], text classification [33], gene expression [51], micro-blogs [82], biological data handling [38], unstructured text data mining with fault classification [75], video…”
BackgroundA Survey of Optimization Methods From a Machine Learning Perspective
Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2950779 · Cited by 789
Self-citationDeep Learning for Extreme Multi-label Text Classification
Liu, J., Chang, W.-C., Wu, Y., & Yang, Y. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080834 · Cited by 676
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Hashimoto, K., xiong, C., Tsuruoka, Y., & Socher, R. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1206 · Cited by 535
Metadata
- Authors
- Lai, S., Xu, L., Liu, K., & Zhao, J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2015-02-19
- Volume
- 29
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2265846598
- Cited by
- 2,280
- References
- 3
- FWCI
- 70.45
- Type
- conference paper
- Language
- en
- Open access
- diamond
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