A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
Table 1: Accuracy (AUC for Irony) achieved by SVM with different feature sets. bowSVM: uni- and bi-gram features. wvSVM: a naive word2vec-based representation, i.e., the average (300-dimensional) word vector for each sentence. bowwvSVM: concatenates bow vectors with the average word2vec representations.
['[BOLD] Dataset', '[BOLD] bowSVM', '[BOLD] wvSVM', '[BOLD] bowwvSVM']
To provide a point of reference for the CNN results, we first report the performance achieved using SVM for sentence classification. As a baseline, we used a linear kernel SVM exploiting uni- and bi-gram features. We also experimented with combining the uni-gram, bi-gram and word vector features with a linear kernel SVM. We kept only the most frequent 30k n-grams for all datasets, and tuned hyperparameters via nested cross-fold validation, optimizing for accuracy (AUC for Irony). We report means from 10-folds over all datasets in Table Notably, even naively incorporating word2vec embeddings into feature vectors usually improves results.