Information Access via Topic Hierarchies and Thematic Annotations from Document Collections

With the development and the availability of large textual corpora, there is a need for enriching and organizing these corpora so as to make easier the research and navigation among the documents. The Semantic Web research focuses on augmenting ordinary Web pages with semantics. Indeed, wealth of information exists today in electronic form, they cannot be easily processed by computers due to lack of external semantics. Furthermore, the semantic addition is an help for user to locate, process information and compare documents contents. For now, Semantic Web research has been focused on the standardization, internal structuring of pages, and sharing of ontologies in a variety of domains. Concerning external structuring, hypertext and information retrieval communities propose to indicate relations between documents via hyperlinks or by organizing documents into concepts hierarchies, both being manually developed. We consider here the problem of automatically structuring and organizing corpora in a way that reflects semantic relations between documents. We propose an algorithm for automatically inferring concepts hierarchies from a corpus. We then show how this method may be used to create specialization/generalization links between documents leading to document hierarchies. As a byproduct, documents are annotated with keywords giving the main concepts present in the documents. We also introduce numerical criteria for measuring the relevance of the automatically generated hierarchies and describe some experiments performed on data from the LookSmart and New Scientist web sites.