Professor Dr. Kevin Ashley of the University of Pittsburgh gave a presentation entitled Toward Integrating Computational Models and Legal Texts , on 6 February 2014 at Stanford Law School.
Click here for video of the presentation.
The talk was part of the CodeX Speaker Series, sponsored by Stanford CodeX: Center for Legal Informatics.
Here is a summary:
This talk will survey some techniques and prospects for enabling computational models of legal reasoning to work directly and automatically with legal texts to perform legal problem-solving tasks. For example, users of commercial legal information retrieval (IR) systems often want to retrieve not merely sentences with highlighted terms, but arguments and argument-related information, that is, argument retrieval (AR). The talk will illustrate how argument-relevant information could be extracted and applied to retrieve arguments from legal decisions. A second example involves techniques for annotating state statutory texts in a particular domain (dealing with public health emergencies), both manually and using machine learning, so that policy analysts can compare states’ regulatory schemes using network analysis. Related AI and Law work on information extraction from cases and statutes will be highlighted.
Filed under: Applications, Presentations, Technology developments, Videos Tagged: Artificial intelligence and law, CodeX Speaker Series, CodeX: The Stanford Center for Legal Informatics, Computational models of legal reasoning, Kevin Ashley, Legal argument extraction, Legal argument retrieval, Legal information extraction, Legal information retrieval, Legal text annotation, Legal text mining, Legal text processing, Legislative network analysis, Machine learning, Machine learning in legal text annotation, Modeling legal arguments, Modeling legal reasoning, Network analysis, Network analysis in legal informatics, Regulatory network analysis
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