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 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.

Abstract. 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.