Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization

Jonghoon Kim, François Rousseau, Michalis Vazirgiannis

Abstract: This paper introduces a convolutional sentence kernel based on word embeddings. Our kernel overcomes the sparsity issue that arises when classifying short documents or in case of little training data. Experiments on six sentence datasets showed statistically significant higher accuracy over the standard linear kernel with ngram features and other proposed models.