The Bechdel test is a sequence of three questions designed to assess the presence of women in movies. Many believe that because women are seldom represented in film as strong leaders and thinkers, viewers associate weaker stereotypes with women. In this paper, we present a computational approach to automate the task of finding whether a movie passes or fails the Bechdel test. This allows us to study the key differences in language use and in the importance of roles of women in movies that pass the test versus the movies that fail the test. Our experiments confirm that in movies that fail the test, women are in fact portrayed as less-central and less-important characters.

We are interested in extracting social networks from text. We present a novel annotation scheme for a new type of event, called social event, in which two people participate such that at least one of them is cognizant of the other. We compare our scheme in detail to the ACE scheme. We perform a detailed analysis of interannotator agreement, which shows that our annotations are reliable.

In this paper, we present a formalization of the task of parsing movie screenplays. While researchers have previously motivated the need for parsing movie screenplays, to the best of our knowledge, there is no work that has presented an evaluation for the task. Moreover, all the approaches in the literature thus far have been regular expression based. In this paper, we present an NLP and ML based approach to the task, and show that this approach outperforms the regular expression based approach by a large and statistically significant margin. One of the main challenges we faced early on was the absence of training and test data. We propose a methodology for using well structured screenplays to create training data for anticipated anomalies in the structure of screenplays.