Name
Abstract | ||
|---|---|---|
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 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 vector-space models. |
Year | Venue | DocType |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 | Conference |
Volume | Citations | PageRank |
P14-1 | 5 | 0.42 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Cao | 1 | 548 | 35.60 |
| Sanjeev Khudanpur | 2 | 2155 | 202.00 |