max, min, etc of the word embeddings of the left and right context of the target word, the target itself, and the whole Tweet.
it takes the word vectors of the left, right, target word, and full tweet/sentence/text contexts and performs max, min, average, standard deviation, and product pooling over these contexts to create a feature vector as input to the Support Vector Machine (SVM) classifier.