Modeling law search as prediction
Modeling law search as prediction
复制标题
DOI：
10.1007/s10506-020-09261-5
复制
发表时间：
2020-02
影响因子：
4.1
通讯作者：
Faraz Dadgostari;Mauricio Guim;P. Beling;Michael A. Livermore;D. Rockmore
中科院分区：
文献类型：
--
作者：
Faraz Dadgostari;Mauricio Guim;P. Beling;Michael A. Livermore;D. Rockmore
法律检索是法律推理的基础，在将法律推理作为正式过程进行研究的过程中，其阐明是一个重要的挑战和悬而未决的问题。本文制定了一个数学模型，将法律搜索的行为和认知框架构建为一个连续的决策过程。该模型有两个组成部分：第一，作为搜索空间的法律语料库模型；第二，与该环境兼容的搜索过程（或搜索策略）模型。搜索空间具有早期工作中开发的“多网络”结构——不同网络的交错结构。在本文中，我们开发并正式描述了搜索过程的三个相关模型。然后，我们在美国最高法院意见语料库的子集上实施这些模型，并根据两个基准预测任务评估它们的表现。第一个是根据文档的语义内容来预测文档中的引用。第二个是预测人类用户生成的搜索结果。对于这两个基准，所有搜索模型都优于空模型，而基于学习的模型则优于其他方法。我们的结果表明，通过额外的工作和改进，机器法则搜索可能有潜力达到人类或接近人类的性能水平。
Law search is fundamental to legal reasoning and its articulation is an important challenge and open problem in the ongoing efforts to investigate legal reasoning as a formal process. This Article formulates a mathematical model that frames the behavioral and cognitive framework of law search as a sequential decision process. The model has two components: first, a model of the legal corpus as asearch spaceand second, a model of the search process (orsearch strategy) that is compatible with that environment. The search space has the structure of a “multi-network”—an interleaved structure of distinct networks—developed in earlier work. In this Article, we develop and formally describe three related models of the search process. We then implement these models on a subset of the corpus of U.S. Supreme Court opinions and assess their performance against two benchmark prediction tasks. The first is to predict the citations in a document from its semantic content. The second is to predict the search results generated by human users. For both benchmarks, all search models outperform a null model with the learning-based model outperforming the other approaches. Our results indicate that through additional work and refinement, there may be the potential for machine law search to achieve human or near-human levels of performance.