Representation Of Case Law For Argumentative Reasoning

Modelling argumentation based on legal cases has been a central topic of AI and Law since its very beginnings. The current established view is that facts must be determined on the basis of evidence. Next, these facts must be used to ascribe legally significant predicates (factors and issues) to the case, on the basis of which the outcome can be established. This thesis aims to provide a method to encapsulate the knowledge of bodies of case law from various legal domains using a recent development in AI knowledge representation, Abstract Dialectical Frameworks (ADFs), as the central feature of the design method. Three legal domains in the US Courts are used throughout the thesis: The domain of the Automobile Exception to the Fourth Amendment, which has been freshly analysed in terms of factors in this thesis; the US Trade Secrets domain analysed from well-known legal case-based reasoning systems (CATO and IBP); and the Wild Animals domain analysed extensively in AI and Law. In this work, ADFs play a role akin to that of Entity-Relationship models in the design of database systems to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. The ADFs in this thesis are instantiated from different starting points: factor-based representation of oral dialogues and factor-based analysis of legal opinions. A legal dialogue representation model is defined for the US Supreme Court Oral Hearing dialogues. The role of these hearings is to identify the components that can form the basis of an argument that will resolve the case. Dialogue moves used by participants have been identified as the dialogue proceeds to assert and modify argument components in term of issues, factors and facts, and to produce what are called Argument Component Trees (ACTs) for each participant in the dialogue, showing how these components relate to one another. The resulting trees can be then merged and used as input to decide the accepted components using an ADF. The model is illustrated using two landmark case studies in the Automobile Exception domain: Carney v. California and US v. Chadwick. A legal justification model is defined to capture knowledge in a legal domain and to provide justification and transparency of legal decisions. First, a legal domain ADF is instantiated from the factor hierarchy of CATO and IBP, then the method is applied to the other two legal domains. In each domain, the cases are expressed in terms of factors organised into an ADF, from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. The proposed method is evaluated to test the ease of implementation, the efficacy of the resulting program, the ease of refinement, transparency of the reasoning and transferability across legal domains. This evaluation suggests ways of improving the decision by incorporating the case facts, and considering justification and reasoning using portions of precedents. The final result is ANGELIC (ADF for kNowledGe Encapsulation of Legal Information from Cases), a method for producing programs that decide the cases with a high degree of accuracy in multiple domains

Defeasible Logic Programming: An Argumentative Approach

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, ie, the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.Comment: 43 pages, to appear in the journal "Theory and Practice of Logic Programming

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Assessing relevance

This paper advances an approach to relevance grounded on patterns of material inference called argumentation schemes, which can account for the reconstruction and the evaluation of relevance relations. In order to account for relevance in different types of dialogical contexts, pursuing also non-cognitive goals, and measuring the scalar strength of relevance, communicative acts are conceived as dialogue moves, whose coherence with the previous ones or the context is represented as the conclusion of steps of material inferences. Such inferences are described using argumentation schemes and are evaluated by considering 1) their defeasibility, and 2) the acceptability of the implicit premises on which they are based. The assessment of both the relevance of an utterance and the strength thereof depends on the evaluation of three interrelated factors: 1) number of inferential steps required; 2) the types of argumentation schemes involved; and 3) the implicit premises required

A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201