NLP

In US State government legislatures, most activity occurs in committees made of lawmakers discussing bills. This paper presents systems to extract legislators' engagement and absence during committee meetings and the stance and affiliation of non-lawmakers making public comments. We propose a system to track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.

Government transparency challenges exist in many forms but at the level of US State legislatures, but they are particularly egregious as public hearings are not documented in text. No official written record of public legislative meetings exists in any state legislature in America. Although a number of states have close captioned videos, these are not accurate or searchable. They are only in English. There is also no identification of speakers, bills, votes, locations, committees, etc.

In US State government legislatures, most of the activity occurs in committees made up of lawmakers discussing bills. When analyzing, classifying or summarizing these committee proceedings, some important features become broadly interesting. In this paper, we engineer four useful features, two applying to lawmakers (engagement and absence), and two to non-lawmakers (stance and affiliation). We propose a system to automatically track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.

Project Panoptyk is a game engine designed to run Massive Multiplayer Online (MMO) games with information creation, sharing, and exchange as the central gameplay focus. This engine is a work in progress, intended to serve as a platform for simulating human/robot interaction, as well as automatic generation of game assets, quests, and real-estate. The project also aims to create an open platform allowing indie and research communities to experiment with MMO concepts.

We present a study using the Digital Democracy transcription tool. Human transcribers work to up-level and annotate California state legislative proceedings using the tool. Four phases of UI and functionality improvements are introduced and for each phase, the resulting change in efficiency is measured and presented.

In this work, we seek to quantify the extent to which a legislator's spoken language indicates their degree of alignment toward an organization that has a taken a documented position on some legislation. To perform this study, we use a corpus of bill discussion transcripts provided by Digital Democracy1. We then apply proven learning methods in the field of natural language processing to predict alignment scores between each member of the California state legislature and a select set of state-recognized organizations.

Government relationships can be complex and difficult to understand. The relationships between members of a legislature, bills, votes and lobbyists who promote various causes are important to understand in representative democracies, but difficult to retrieve using current methods. In this paper, we propose a 3D visualization system to explore such legislative relationships for users. We use real data from California state legislature obtained from the Digital Democracy project.