Vertical Domain Text Classification: Towards Understanding IT Tickets Using Deep Neural Networks

AAAI 2018 pp. 8202-8203

doi:10.1609/AAAI.V32I1.11375 /aaai/2018/han2018aaai-vertical/

Abstract

It is challenging to directly apply text classification models without much feature engineering on domain-specific use cases, and expect the state of art performance. Much more so when the number of classes is large. Convolutional Neural Network (CNN or Con-vNet) has attracted much in text mining due to its effectiveness in automatic feature extraction from text. In this paper, we compare traditional and deep learning approaches for automatic categorization of IT tickets in a real-world production ticketing system. Experimental results demonstrate the good potential of CNN models in our task.

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Cite

Text

Han and Akbari. "Vertical Domain Text Classification: Towards Understanding IT Tickets Using Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11375

Markdown

[Han and Akbari. "Vertical Domain Text Classification: Towards Understanding IT Tickets Using Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/han2018aaai-vertical/) doi:10.1609/AAAI.V32I1.11375

BibTeX

@inproceedings{han2018aaai-vertical,
  title     = {{Vertical Domain Text Classification: Towards Understanding IT Tickets Using Deep Neural Networks}},
  author    = {Han, Jianglei and Akbari, Mohammad},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8202-8203},
  doi       = {10.1609/AAAI.V32I1.11375},
  url       = {https://mlanthology.org/aaai/2018/han2018aaai-vertical/}
}