NLP

This research concerns the perceived need for and benefits of an algorithmically generated, personalizable tip sheet that could be used by journalists to improve and expand coverage of state legislatures. This study engaged in two research projects to understand if working journalists could make good use of such a tool and, if so, what features and functionalities they would most value within it. This study also explored journalists’ perceptions of the role of such tools in their newswork.

Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information during such proceedings. Even if professional-quality automated transcriptions existed, other tasks such as speaker or rhetorical position identifications are not fully automatable. This work focuses on improving and evaluating the transcription software used by the Digital Democracy initiative, named Transcription Tool.

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user’s text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with.

Algorithmic journalism refers to automatic AI-constructed news stories. There have been successful commercial implementations for news stories in sports, weather, financial reporting and similar domains with highly structured, well defined tabular data sources. Other domains such as local reporting have not seen adoption of algorithmic journalism, and thus no automated reporting systems are available in these categories which can have important implications for the industry.

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.