Ogmios: a scalable NLP platform for annotating large web document collections

Thierry Hamon (1) , Julien Derivière (1) , Adeline Nazarenko (1)

1 LIPN - Laboratoire d'Informatique de Paris-Nord

Web semantic access in specific domains calls for specialised search engines with enhanced semantic querying and indexing capacities, which pertain both to information retrieval (IR) and to information extraction (IE). A rich linguistic analysis is required either to identify the relevant semantic units to index and weight them according to linguistic specific statistical distribution, or as the basis of an information extraction process. Recent developments make Natural Language Processing (NLP) techniques reliable enough to process large collections of documents and to enrich them with semantic annotations. This paper focuses on the design and the development of a text processing platform, Ogmios, which has been developed in the ALVIS project. The Ogmios platform exploits existing NLP modules and resources, which may be tuned to specific domains and produces linguistically annotated documents. We show how the three constraints of genericity, domain semantic awareness and performance can be handled all together.

Domaines: Intelligence artificielle [cs.AI]; Traitement du texte et du document; Recherche d'information [cs.IR]

Thierry Hamon, Julien Derivière, Adeline Nazarenko. Ogmios: a scalable NLP platform for annotating large web document collections. Corpus Linguistics, Jul 2007, Manchester, United Kingdom. pp.195-208. ⟨hal-00619260⟩