Convolution Kernels for Discriminative Learning from Streaming Text

AAAI 2016 pp. 2757-2763

doi:10.1609/AAAI.V30I1.10348 /aaai/2016/lukasik2016aaai-convolution/

Abstract

Time series modeling is an important problem with many applications in different domains. Here we consider discriminative learning from time series, where we seek to predict an output response variable based on time series input. We develop a method based on convolution kernels to model discriminative learning over streams of text. Our method outperforms competitive baselines in three synthetic and two real datasets, rumour frequency modeling and popularity prediction tasks.

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Cite

Text

Lukasik and Cohn. "Convolution Kernels for Discriminative Learning from Streaming Text." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10348

Markdown

[Lukasik and Cohn. "Convolution Kernels for Discriminative Learning from Streaming Text." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/lukasik2016aaai-convolution/) doi:10.1609/AAAI.V30I1.10348

BibTeX

@inproceedings{lukasik2016aaai-convolution,
  title     = {{Convolution Kernels for Discriminative Learning from Streaming Text}},
  author    = {Lukasik, Michal and Cohn, Trevor},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {2757-2763},
  doi       = {10.1609/AAAI.V30I1.10348},
  url       = {https://mlanthology.org/aaai/2016/lukasik2016aaai-convolution/}
}