Learning Structural Kernels for Natural Language Processing
Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better).
['[EMPTY]', 'Pearson’s']
The best GP model with tree kernels outperforms the SVMs, showing that the fine-grained model selection procedure provided by the GP models is helpful when dealing with tree kernels. However, using the SASSTK models do not help in the case of free α and the SASSTKfull actually performs worse than the original SSTK, even though the optimized marginal likelihood was higher. This is evidence that the SASSTKfull model is overfitting the training data, probably due to its large number of hyperparameters. Inspecting the free α models showed that the values found by the optimizer were very close to zero. This indicates that the model selection procedure prefer towards giving smaller weights to incomplete tree fragments. We can interpret this as the model selecting a more lexicalized feature space, which also explains why the GP RBF model on bag-of-words performed the best in this task.