Online learning in tensor space

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Abstract

We propose an online learning algorithm based on tensor-space models. A tensorspace model represents data in a compact way, and via rank-1 approximation the weight tensor can be made highly structured, 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 defined but very limited resources are available for training. We apply with the proposed algorithm to a parsing task, and show that even with very little training data the learning algorithm based on a tensor model performs well, and gives significantly better results than standard learning algorithms based on traditional vectorspace models. © 2014 Association for Computational Linguistics.

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APA

Cao, Y., & Khudanpur, S. (2014). Online learning in tensor space. In 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference (Vol. 1, pp. 666–675). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p14-1063

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