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

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...