NLP

In story or character driven games, in-game stories are usually manually authored in advance. As the complexity of interactions in games increases, the quantity of hand-crafted text typically follows. Designing stories and composing content by hand is a laborious and time consuming process that if automated, would speed up game production and lower development costs. In this paper, we present a mixed initiative tool to help generalize and enhance context free grammars (CFGs) for story generation.

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. We show this is also possible to do automatically using machine learning, potentially providing a powerful tool to ordinary citizens. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree.

A first-of-its-kind platform, Digital Democracy1 offers a searchable archive of all statements made in US state legislative hearings in four American states (California, New York, Texas and Florida) covering one third of the US population. The purpose of the platform is to increase government transparency in state legislatures. It allows citizens to follow state lawmakers, lobbyists, and advocates as they debate, craft, and vote on policy proposals. State hearings in the U.S. are typically recorded on video and broadcast on cable TV stations, but they are not transcribed or indexed.

Our main thesis is that computational processing of natural language styles can be accomplished using corpus analysis methods and language transformation rules. We demonstrate this first by statistically modeling natural language styles, and second by developing tools that carry out style processing, and finally by running experiments using the tools and evaluating the results.