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

Touria Aït El Mekki (1) , Adeline Nazarenko (2)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 LERIA - Laboratoire d'Etudes et de Recherche en Informatique d'Angers

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.

Mots clés: Natural Language Processing; indexing

Domaines: Intelligence artificielle [cs.AI]; Recherche d'information [cs.IR]; Informatique

Touria Aït El Mekki, Adeline Nazarenko. Using NLP to build the hypertextuel network of a back-of-the-book index. 2005, pp.316-320. ⟨hal-00098036⟩