Showing papers by "Marie-Francine Moens published in 2003"

Concept extraction from legal cases: the use of a statistic of coincidence

Abstract: Effective retrieval of court decisions is important. Automatically identifying legal concepts in the decision texts would be very helpful. In this paper we investigate how a statistics for hypothesis testing, i.e., the likelihood ratio, can help in this task. We describe how this statistic can be used for detecting important multi-term phrases in the case texts, how it can be used to find correlated terms, and how it is a means for feature or topic signature selection in automated case categorization. The technology has been tested upon more than 600 US cases.

Summarization of texts found on the world wide web

Abstract: Summaries of texts found on the World Wide Web are valuable. They help the user of a search engine to select information and are an aid for processing the vast amount of information found on the Web. This chapter describes the technologies that can be applied for summarizing the texts of Web pages. The focus is on technologies that currently generate the best results and are suited for the specific heterogeneous environment that makes up the World Wide Web. This chapter gives an overview of generic, query-biased and task-specific summarization, as well as single-document and multi-document summarization. Among the technologies that are discussed are semantic frame technologies, rhetorical structure analysis, learning discourse patterns, techniques relying upon lexical cohesion, and text clustering.