Marie-Francine Moens

Automatic detection of arguments in legal texts

This paper provides the results of experiments on the detection of arguments in texts among which are legal texts. The detection is seen as a classification problem. A classifier is trained on a set of annotated arguments. Different feature sets are evaluated involving lexical, syntactic, semantic and discourse properties of the texts. The experiments are a first step in the context of automatically classifying arguments in legal texts according to their rhetorical type and their visualization for convenient access and search.

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.

Approaches to text mining arguments from legal cases

This paper describes recent approaches using text-mining to automatically profile and extract arguments from legal cases. We outline some of the background context and motivations. We then turn to consider issues related to the construction and composition of corpora of legal cases. We show how a Context-Free Grammar can be used to extract arguments, and how ontologies and Natural Language Processing can identify complex information such as case factors and participant roles. Together the results bring us closer to automatic identification of legal arguments.

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, specially legal reports.

Generating a Topic Hierarchy from Dialect Texts

We built a system for the automatic creation of a text- based topic hierarchy, meant to be used in a geographically defined community. This poses two main problems. First, the appearance of both standard language and a community-related dialect, demanding that dialect words should be as much as possible corrected to standard words, and second, the automatic hierarchic clustering of texts by their topic. The problem of correcting dialect words is dealt with by performing a nearest neighbor search over a dynamic set of known words, using a set of transition rules from dialect to standard words, which are learned from a parallel corpus. We solve the clustering problem by implementing a hierarchical co-clustering algorithm that automatically generates a topic hierarchy of the collection and simultaneously groups documents and words into clusters.

A Belief Model of Query Difficulty That Uses Subjective Logic

The difficulty of a user query can affect the performance of Information Retrieval (IR) systems. This work presents a formal model for quantifying and reasoning about query difficulty as follows: Query difficulty is considered to be a subjective belief, which is formulated on the basis of various types of evidence. This allows us to define a belief model and a set of operators for combining evidence of query difficulty. The belief model uses subjective logic , a type of probabilistic logic for modeling uncertainties. An application of this model with semantic and pragmatic evidence about 150 TREC queries illustrates the potential flexibility of this framework in expressing and combining evidence. To our knowledge, this is the first application of subjective logic to IR.