The function for the negation handler is available at my Github repo. An example of the function output is shown below. 'Negation' is the main function being called on the tokenized sentence as shown. In the function, whenever a negation word (like'not', "n't", 'non-', 'un-', etc) is encountered, a set of cognitive synonyms called synsets are generated for the word next to the negation. These synsets are interlinked by conceptual semantic and lexical relations to each other in a lexical database called WordNet.

Negation plays a significant role in spoken and written natural languages. Negation is used in language to deny something or to reverse the polarity or the sense of a statement. This paper presents a novel approach to automatically handling negation in tutorial dialogues using deep learning methods. In particular, we explored various Long Short Term Memory (LSTM) models to automatically detect negation focus, scope and cue in tutorial dialogues collected from experiments with actual students interacting with the state-of-the-art intelligent tutoring system, DeepTutor. The results obtained are promising.