Paper: Online Learning in Tensor Space

ACL ID P14-1063
Title Online Learning in Tensor Space
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We propose an online learning algorithm based on tensor-space models. A tensor- space model represents data in a compact way, and via rank-1 approximation the weight tensor can be made highly struc- tured, resulting in a significantly smaller number of free parameters to be estimated than in comparable vector-space models. This regularizes the model complexity and makes the tensor model highly effective in situations where a large feature set is de- fined but very limited resources are avail- able for training. We apply with the pro- posed algorithm to a parsing task, and show that even with very little training data the learning algorithm based on a ten- sor model performs well, and gives signif- icantly better results than standard learn- ing algorithms based on traditional vector- space...