is the standard word counting method whereby the feature vector represents the term frequency of the words in a sentence. is similar to BoW, except that it is derived by the counting of the words in the sentence weighted by individual word's term-frequency and inverse-document-frequency BIBREF31 . is derived from clustering of words-embeddings with k-means into 5000 clusters, and follow by BoW representation of the words in 5000 clusters. 
 Question: What other non-neural baselines do the authors compare to? 
bag of words, tf-idf, bag-of-means