Showing papers by "Marie-Francine Moens published in 2001"

Innovative techniques for legal text retrieval

Abstract: Legal text retrieval traditionally relies upon external knowledge sources such as thesauri and classification schemes, and an accurate indexing of the documents is often manually done. As a result not all legal documents can be effectively retrieved. However a number of current artificial intelligence techniques are promising for legal text retrieval. They sustain the acquisition of knowledge and the knowledge-rich processing of the content of document texts and information need, and of their matching. Currently, techniques for learning information needs, learning concept attributes of texts, information extraction, text classification and clustering, and text summarization need to be studied in legal text retrieval because of their potential for improving retrieval and decreasing the cost of manual indexing. The resulting query and text representations are semantically much richer than a set of key terms. Their use allows for more refined retrieval models in which some reasoning can be applied. This paper gives an overview of the state of the art of these innovativetechniques and their potential for legal text retrieval.

Generic topic segmentation of document texts

Abstract: Topic segmentation is an important initial step in many text-based tasks. A hierarchical representation of a texts topics is useful in retrieval and allows judging relevancy at different levels of detail. This short paper describes research on generic algorithms for topic detection and segmentation that are applicable on texts of heterogeneous types and domains.