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

• URL: http://arxiv.org/abs/2109.08855v1
• Date: Sat, 18 Sep 2021 06:50:35 GMT
• Title: Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees
• Authors: Josh Grace and Foaad Khosmood
• Abstract summary: We propose a system to automatically track the affiliation of organizations in public comments. A metric to compute legislator engagement and absenteeism is also proposed.
• Score: 0.8122270502556374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The model tracking affiliation achieves an F1 of 0.872 while the support determination has an F1 of 0.979. Additionally, a metric to compute legislator engagement and absenteeism is also proposed and as proof-of-concept, a list of the most and least engaged legislators over one full California legislative session is presented.

Related papers

• LegiGPT: Party Politics and Transport Policy with Large Language Model [0.0]
  This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals.<n>Political affiliations and sponsor characteristics were used to identify key factors shaping transportation policymaking.<n>Results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes.
  arXiv  Detail & Related papers  (2025-06-20T02:25:52Z)
• CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models [30.288227578616905]
  We introduce a corporate governance reasoning benchmark (CHANCERY) to test a model's ability to reason about whether executive/board/shareholder's proposed actions are consistent with corporate governance charters.<n>The benchmark consists of a corporate charter (a set of governing covenants) and a proposal for executive action.<n> Evaluations on state-of-the-art (SOTA) reasoning models confirm the difficulty of the benchmark, with models such as Claude 3.7 Sonnet and GPT-4o achieving 64.5% and 75.2% accuracy respectively.
  arXiv  Detail & Related papers  (2025-06-05T05:13:32Z)
• AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios [47.83822985839837]
  We present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases.<n>The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance.<n> Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario.
  arXiv  Detail & Related papers  (2025-05-22T10:50:33Z)
• Measuring Interest Group Positions on Legislation: An AI-Driven Analysis of Lobbying Reports [17.4092661362727]
  Special interest groups ( SIGs) in the U.S. participate in a range of political activities to influence policy decisions in the legislative and executive branches. Despite the significance of understanding SIGs' policy positions, empirical challenges in observing them have led researchers to rely on indirect measurements. This study introduces the first large-scale effort to directly measure and predict a wide range of bill positions.
  arXiv  Detail & Related papers  (2025-04-21T17:54:47Z)
• A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences [76.73731245899454]
  We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the Intelligent Court''
  arXiv  Detail & Related papers  (2025-03-02T10:26:54Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• The Ethics of Automating Legal Actors [58.81546227716182]
  We argue that automating the role of the judge raises difficult ethical challenges, in particular for common law legal systems. Our argument follows from the social role of the judge in actively shaping the law, rather than merely applying it. Even in the case the models could achieve human-level capabilities, there would still be remaining ethical concerns inherent in the automation of the legal process.
  arXiv  Detail & Related papers  (2023-12-01T13:48:46Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• SemEval 2023 Task 6: LegalEval - Understanding Legal Texts [2.172613863157655]
  There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document, Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for
  arXiv  Detail & Related papers  (2023-04-19T10:28:32Z)
• Large Language Models as Corporate Lobbyists [0.0]
  Autoregressive large language model determines if proposed U.S. Congressional bills are relevant to specific public companies. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation.
  arXiv  Detail & Related papers  (2023-01-03T16:25:52Z)
• DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction [0.0]
  This paper introduces DeepParliament, a legal domain Benchmark dataset that gathers bill documents and metadata. We propose two new benchmarks: Binary and Multi-Class Bill Status classification. This work will be the first to present a Parliament bill prediction task.
  arXiv  Detail & Related papers  (2022-11-15T04:55:32Z)
• Election of government ministers [0.0]
  We propose a scenario where government members are directly elected by the people. In this article, we will present the implementation of the algorithm (GreedyPAV) proposed by Rutvik Page, Ehud Shapiro, and Nimrod Talmon.
  arXiv  Detail & Related papers  (2022-10-13T07:26:38Z)
• Disentangling Active and Passive Cosponsorship in the U.S. Congress [0.09236074230806579]
  In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. We develop anRGC+N based model that learns legislator representations from bill texts and speech transcripts.
  arXiv  Detail & Related papers  (2022-05-19T16:33:46Z)
• Transparency, Compliance, And Contestability When Code Is(n't) Law [91.85674537754346]
  Both technical security mechanisms and legal processes serve as mechanisms to deal with misbehaviour according to a set of norms. While they share general similarities, there are also clear differences in how they are defined, act, and the effect they have on subjects. This paper considers the similarities and differences between both types of mechanisms as ways of dealing with misbehaviour.
  arXiv  Detail & Related papers  (2022-05-08T18:03:07Z)
• A Methodology for Creating AI FactSheets [67.65802440158753]
  This paper describes a methodology for creating the form of AI documentation we call FactSheets. Within each step of the methodology, we describe the issues to consider and the questions to explore. This methodology will accelerate the broader adoption of transparent AI documentation.
  arXiv  Detail & Related papers  (2020-06-24T15:08:59Z)
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence [81.04070052740596]
  Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
  arXiv  Detail & Related papers  (2020-04-25T14:45:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.