sentence boundaries and, thus, include several sentences. In other words, sequences of conditions and recommandations correspond to *discourse* structures. Discourse processing requires the recognition of heterogeneous linguistic features (especially, the granularity of relevant features may vary according to text genre [9]). Following these observations, we made a study based on a representative corpus and automatic text mining techniques, in order to semi-automatically discover relevant linguistic features for the task and infer the rules necessary to accurately structure the practice guidelines. The paper is organized as follow: first, we present the task and some previous approaches (section 2). We then describe the rules for text structuring (section 3) and the method used to infer them. We finish with the presentation of some results (section 4), before the conclusion.

## 2 Document Restructuring: The Case Of Practice Guidelines

As we have previously seen, practice guidelines are not routinely fully exploited. To overcome this problem, national health agencies try to promote the electronic distribution of these guidelines.

## 2.1 Previous Work

Several attempts have already been made to improve the use of practice guidelines. For example, knowledge-based diagnostic aids can be derived from them [3]. GEM is an intermediate document model, between pure text (paper practice guidelines) and knowledge-based models like GLIF [4]. GEM is thus an elegant solution, independent from any theory or formalisms, but compliant with other frameworks. Previous attempts to automate the translation process between the text and GEM are based on the analysis of isolated sentences and do not compute the exact scope of conditional segments [5]. 

## 2.2 Our Approach

Our aim is to semi-automatically fill a GEM template from existing guidelines: the algorithm is fully automatic but the result needs to be validated by experts to yield adequate accuracy. We first focus on the most important part of the GEM DTD
knowledge Component which is sequences of conditions and recommandations. 

We propose a two-step strategy: 1) basic segments (conditions and recommendations) are recognized and 2) the scope of the conditional segments is computed. In this paper, we focus on the second step, which is the most difficult one and has not been solved by previous systems. What is obtained in the end is a tree where the leaves are recommendations and the branching nodes are conditional segments, as shown on Figure 1. All the children of a node are under the scope of the parent node.