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.

An application-oriented terminology evaluation: the case of back-of-the book indexes

This paper addresses the problem of computational terminology evaluation not per se but in a specific application context. This paper describes the evaluation procedure that has been used to assess the validity of our overall indexing approach and the quality of the IndDoc indexing tool. Even if user-oriented extended evaluation is irreplaceable, we argue that early evaluations are possible and they are useful for development guidance.