This paper reports results from the JusticeBot Project, in which we analyzed two datasets drawn f... more This paper reports results from the JusticeBot Project, in which we analyzed two datasets drawn from 1 million written decisions from the Régie du logement du Québec. Using an empirical methodology, we identified 44 factors that occur in disputes where the tenant seeks a remedy due to problems with the rented apartment, such as the existence of bedbugs, high noise levels or problems with insulation. In the first dataset, we used these factors to tag 149 cases. We found a correlation between how many factors are found in a case and how likely the judge is to award rent reduction to a tenant; the amount of reduction was also higher in cases with more factors. For the second dataset (39 cases with bedbugs, drawn from the first dataset), we developed in-depth factors and used them to tag the cases. We found a number of plausible correlations, such as the average damage award being higher in cases with infestations of high intensity. Finally, in predicting the decision of the judge using the factors present in a case, the results were similar to the baselines or slightly above. We discuss the possible reasons for this, and why the approach shows promise in providing useful information to lay people and lawyers.
This paper announces the creation and public availability of a dataset of annotated decisions adj... more This paper announces the creation and public availability of a dataset of annotated decisions adjudicating claims by military veterans for disability compensation in the United States. This is intended to initiate a collaborative, transparent approach to semantic analysis for argument mining from legal documents. The dataset is being used in the LUIMA argument-mining project. We address two major sub-tasks for making legal reasoning computable. First, we report the semantic types of propositional connective we use to extract information about legal rules from sentences in statutes, regulations, and appellate court decisions, and to represent those rules as integrated systems. Second, we report the semantic types of sentence role we use to extract and represent the fact-finding reasoning found in adjudicatory decisions, with the goal of identifying successful and unsuccessful patterns of evidentiary argument. For each type system, we provide explanations and examples. Thus, we hope to stimulate a shared effort to create diverse datasets in law, to empirically evolve optimal sets of semantic types for argument mining, and to refine protocols for accurately applying those types to texts.
International Conference on Legal Knowledge and Information Systems, 2013
Users of commercial legal information retrieval (IR) systems often want argument retrieval (AR): ... more Users of commercial legal information retrieval (IR) systems often want argument retrieval (AR): retrieving not merely sentences with highlighted terms, but arguments and argument-related information. Using a corpus of argumentannotated legal cases, we conducted a baseline study of current legal IR systems in responding to standard queries. We identify ways in which they cannot meet the need for AR and illustrate how additional argument-relevant information could address some of those inadequacies. We conclude by indicating our approach to developing an AR system to retrieve arguments from legal decisions.
This paper reports results from the JusticeBot Project, in which we analyzed two datasets drawn f... more This paper reports results from the JusticeBot Project, in which we analyzed two datasets drawn from 1 million written decisions from the Régie du logement du Québec. Using an empirical methodology, we identified 44 factors that occur in disputes where the tenant seeks a remedy due to problems with the rented apartment, such as the existence of bedbugs, high noise levels or problems with insulation. In the first dataset, we used these factors to tag 149 cases. We found a correlation between how many factors are found in a case and how likely the judge is to award rent reduction to a tenant; the amount of reduction was also higher in cases with more factors. For the second dataset (39 cases with bedbugs, drawn from the first dataset), we developed in-depth factors and used them to tag the cases. We found a number of plausible correlations, such as the average damage award being higher in cases with infestations of high intensity. Finally, in predicting the decision of the judge using the factors present in a case, the results were similar to the baselines or slightly above. We discuss the possible reasons for this, and why the approach shows promise in providing useful information to lay people and lawyers.
This paper announces the creation and public availability of a dataset of annotated decisions adj... more This paper announces the creation and public availability of a dataset of annotated decisions adjudicating claims by military veterans for disability compensation in the United States. This is intended to initiate a collaborative, transparent approach to semantic analysis for argument mining from legal documents. The dataset is being used in the LUIMA argument-mining project. We address two major sub-tasks for making legal reasoning computable. First, we report the semantic types of propositional connective we use to extract information about legal rules from sentences in statutes, regulations, and appellate court decisions, and to represent those rules as integrated systems. Second, we report the semantic types of sentence role we use to extract and represent the fact-finding reasoning found in adjudicatory decisions, with the goal of identifying successful and unsuccessful patterns of evidentiary argument. For each type system, we provide explanations and examples. Thus, we hope to stimulate a shared effort to create diverse datasets in law, to empirically evolve optimal sets of semantic types for argument mining, and to refine protocols for accurately applying those types to texts.
International Conference on Legal Knowledge and Information Systems, 2013
Users of commercial legal information retrieval (IR) systems often want argument retrieval (AR): ... more Users of commercial legal information retrieval (IR) systems often want argument retrieval (AR): retrieving not merely sentences with highlighted terms, but arguments and argument-related information. Using a corpus of argumentannotated legal cases, we conducted a baseline study of current legal IR systems in responding to standard queries. We identify ways in which they cannot meet the need for AR and illustrate how additional argument-relevant information could address some of those inadequacies. We conclude by indicating our approach to developing an AR system to retrieve arguments from legal decisions.
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Papers by Vern R. Walker