Convolution Kernels for Discriminative Learning from Streaming Text
DOI:
https://doi.org/10.1609/aaai.v30i1.10348Abstract
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.
Downloads
Published
2016-03-05
How to Cite
Lukasik, M., & Cohn, T. (2016). Convolution Kernels for Discriminative Learning from Streaming Text. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10348
Issue
Section
Technical Papers: NLP and Machine Learning