While researching spatial movements in play-scripts, we uncovered some movements that actors performed that could not be explained by the annotations or basic theatre rules. Here, we look to learn implied motion based on what the characters in the play are saying. Humans are able to do this with only being given the play-script, so how do we get a computer to do it? Several features, including n -grams, parts of speech (POS) bag of words (BoW), length of speech, and other contextual details were utilized with several machine learning methods to help predict movement within the play. Results reveal that is a difficult problem and basic natural language processing (NLP) and machine learning (ML) techniques do not perform much better than a random classifier.

Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audience based on its geographic location is a major challenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a performance improvement of three orders by TwitterTagger compared to that of the baseline model.