NLP

Evaluation of Automatic Text Summarization using Synthetic Facts

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.

Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees

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.

Panoptyk: information driven MMO engine

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.

Gaining efficiency in human assisted transcription and speech annotation in legislative proceedings

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.

Learning alignments from legislative discourse

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.

3D visualization of legislative relationships

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.

Exploring Reporter-Desired Features for an AI-Generated Legislative News Tip Sheet

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.

Deconstructing Human Assisted Video Transcription and Annotation for Legislative Proceedings

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.

An Empathetic AI Coach for Self-Attachment Therapy

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.

Automatic News Article Generation from Legislative Proceedings: A Phenom-based Approach

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.