The Research Laboratory for Law, Logic and Technology (LLT Lab) conducts empirical research on argument and reasoning patterns in diverse samples of legal documents, creating the semantic data, training protocols, and software analytics needed to develop technology to assist legal reasoning.
Veterans Claims
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Veterans Claims Project
Goal: Developing technology to assist human review of arguments and reasoning about claims for veterans benefits.
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VetClaim Corpus
Product: Semantic data consisting of annotated administrative and judicial decisions, useful for developing automation through machine learning or rule-based programming.
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Veterans Claims Reasoning
Product: Argument structures that are either generic to legal reasoning, or specific to policy-based reasoning or evidence-based reasoning in claims for veterans' disability benefits.
Vaccine/Injury Compensation
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Vaccine/Injury Project
Goal: Developing technology to assist human review of arguments and reasoning about awarding compensation for vaccine-related injuries.
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V/IP Corpus
Product: Semantic data consisting of annotated judicial decisions, useful for developing automation through machine learning or rule-based programming.
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V/IP Reasoning
Product: Argument structures that are either generic to legal reasoning, or specific to policy-based reasoning under the vaccine statute.
Medical Malpractice Compensation
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Medical Malpractice Project
Goal: Developing technology to assist human review of arguments and reasoning about awarding compensation for medical malpractice claims.
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MedMal Corpus
Product: Semantic data consisting of annotated judicial decisions, useful for developing automation through machine learning or rule-based programming.
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Medical Malpractice Reasoning
Product: Argument structures that are either generic to legal reasoning, or specific to policy-based reasoning in medical malpractice cases.
