Using NLP to build the hypertextuel network of a back-of-the-book index

Relying on the idea that back-of-the-book indexes are traditional devices for navigation through large documents, we have developed a method to build a hypertextual network that helps the navigation in a document. Building such an hypertextual network requires selecting a list of descriptors, identifying the relevant text segments to associate with each descriptor and finally ranking the descriptors and reference segments by relevance order. We propose a specific document segmentation method and a relevance measure for information ranking. The algorithms are tested on 4 corpora (of different types and domains) without human intervention or any semantic knowledge.

• Type: Journal
• Year: 2006
• Where: CORR
• Authors: Touria Aït El Mekki, Adeline Nazarenko
• Keywords: CORR 2006, Document, Education, Hypertextual Network, Relevant Text Segments