Rik De Busser

Generic topic segmentation of document texts

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.

Semantic case role detection for information extraction

If information extraction wants to make its results more accurate, it will have to resort increasingly to a coherent implementation of natural language semantics. In this paper, we will focus on the extraction of semantic case roles from texts. After setting the essential theoretical framework, we will argue that it is possible to detect case roles on the basis of morphosyntactic and lexical surface phenomena. We will give a concise overview of our methodology and of a preliminary test that seems to confirm our hypotheses.

Summarization of texts found on the world wide web

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.