Character-level Convolutional Networks for Text Classification

One of the common natural language understanding problems is text classification. Over last few decades, machine learning researchers have been moving from the simplest "bag of words" model to more sophisticated models for text classification. Bag of words model uses only information about which words are used in the text. Adding TFIDF to the bag of words helps to track relevancy of each word to the document. Bag of n-grams enables using partial information about structure of the text. Recurrent neural networks, like LSTM, can capture dependencies between words even if they are far from each other.