Automatic Detection and Classification of Argumentation in a Legal Case (Automatische detectie en classificatie van de argumentatie in een juridische zaak)

The huge amount of documents available in the legal domain calls for computational tools supporting efficient and intelligent search and filtering of information. Over the last several years, machine-learning oriented research in information retrieval and document classification has spawned a number of systems capable of handling structural content management, helping users to automatically or semi-automatically identify relevant structured portions of legal texts, such as paragraphs, chapters or intertextual references.This PhD thesis explores a novel idea to identify relevant portions of legal texts by using argumentation analysis. Many legal texts are argumentative, such as the case files exchanged by the parties in a case, a court's decision, scholarly publications and discussions and opinions in legal blogs. Therefore, argumentation can be used as a means to structure the texts contents to search and filter their information. However, there has been little research done on the automatic detection of argumentation or its structure.This thesis presents a general way to automatically detect the arguments of a legal text and how they interact. This allows one to obtain a structured representation of the information of the text which later on can be used as a novel means to search or filter documents.To this end this thesis introduces and discusses the development of the first corpus of legal texts fully annotated by their argumentation, including the four stages of the corpus creation process (design, collection, annotation and analysis). It also presents different approaches to obtain an automatic method to detect argumentation in legal cases. All the approaches are based on state-of-the-art information retrieval and natural language processing methods.nrpages: 218status: publishe

Creating an argumentation corpus: do theories apply to real arguments? A case study on the legal argumentation of the ECHR

Argumentation annotation is a crucial step in applying machine learning techniques to the argumentation field. However, there exist few argumentation corpora and their development has not been studied in depth. In this paper we present a study conducted during the creation of a legal argumentation corpus. It shows how well-known argumentation theories are used as the background framework of the annotation process and which difficulties are found when applying those theories to real argumentation. The aim of the paper is to highlight different critical points humans encounter when applying theory to real argumentation, allowing better and faster approaches in future annotation processes. Furthermore, we also highlight fundamental problems of the chosen argumentation theories and thereby offer ideas for future research on argumentation theory.status: publishe

Study on sentence relations in the automatic detection of argumentation in legal cases

We report the results of experiments which prove that the analysis of the discourse relations between sentences increase the accuracy in the automatic detection of argumentation in texts of legal cases. We treat the search of arguments as a classification problem, where a classifier is trained on a set of annotated legal cases. For these experiments we use exclusively legal texts. Our corpus is a human-annotated and automatically extracted test set from a collection of legal cases of the European Court of Human Rights. The corpus has been annotated during four weeks following a strict methodology based on different argumentation schemes. We obtain an increment of around 8% in general classification accuracy compared to previous experiments due to the addition of new features that study the relations between the text sentences.status: publishe

Argumentation mining

Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analysis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining.status: publishe

Automatic argumentation detection and its role in law and the semantic web

The automatic detection of arguments in text regards a relatively new area at the intersection of Natural Language Processing, Information Retrieval and Legal Information Systems. This paper presents some fundamental issues when processing texts that contain argumentation. Furthermore, our research bridges different areas, including the legal field and the Semantic Web, where argumentation detection and reconstruction could be beneficial. Finally, it analyses several methodologies to accomplish this task, providing results from different experiments done over several kinds of texts, especially legal reports.status: publishe

Argumentation mining: the detection, classification and structure of arguments in text

Argumentation is the process by which arguments are constructed and handled. Argumentation constitutes a major component of human intelligence. The ability to engage in argumentation is essential for humans to understand new problems, to perform scientific reasoning, to express, to clarify and to defend their opinions in their daily lives. Argumentation mining aims to detect the arguments presented in a text document, the relations between them and the internal structure of each individual argument. In this paper we analyse the main research questions when dealing with argumentation mining and the different methods we have studied and developed in order to successfully confront the challenges of argumentation mining in legal texts.status: publishe

Study on the structure of argumentation in case law

This paper investigates natural language argumentation in the case law domain. The starting point is a study on the discourse and argumentative characteristics of ten legal documents from the European Court of Human Rights (ECHR). Then, a generalization of this study allows to formalize the structure of argumentation in the ECHR documents as a context-free grammar. The paper concludes with the evaluation of the grammar and a discussion of its main limitations.series: Frontiers in Artificial Intelligence and Applicationsstatus: publishe