Text segmentation based on document understanding for information retrieval

Information retrieval needs to match relevant texts with a given query. Selecting appropriate parts is useful when documents are 4 long, and only portions are interesting to the user. In this paper, we 9 describe a method that extensively uses natural language techniques for text segmentation based on topic change detection. The method requires a NLP-parser and a semantic representation in Roget-based vectors. We have run the experiment on French documents, for which we have the appropriate tools, but the method could be transposed to any other language with the same requirements. The article sketches an overview of the NL understanding environment functionalities, and the algorithms related to our text segmentation method. An experiment in text segmentation is also presented and its result in an information retrieval task is shown.