Wiktionary and NLP: improving synonymy networks

Authors:
Emmanuel Navarro;Franck Sajous;Bruno Gaume;Laurent Prévot;Hsieh ShuKai;Kuo Tzu-Yi;Pierre Magistry;Huang Chu-Ren
Affiliations:
IRIT, CNRS & Universitéé de Toulouse;CLLE-ERSS, CNRS & Universitéé de Toulouse;CLLE-ERSS & IRIT, CNRS & Université& Universitéé de Toulouse;LPL, CNRS & Universitéé de Provence;NTNU, Taiwan;Graduate Institute of Linguistics NTU, Taiwan;TIGP, CLCLP, Academia Sinica, GIL, NTU, Taiwan;Hong Kong Poly U., Hong Kong
Venue:
People's Web '09 Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources
Year:
2009

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Abstract

Wiktionary, a satellite of the Wikipedia initiative, can be seen as a potential resource for Natural Language Processing. It requires however to be processed before being used efficiently as an NLP resource. After describing the relevant aspects of Wiktionary for our purposes, we focus on its structural properties. Then, we describe how we extracted synonymy networks from this resource. We provide an in-depth study of these synonymy networks and compare them to those extracted from traditional resources. Finally, we describe two methods for semi-automatically improving this network by adding missing relations: (i) using a kind of semantic proximity measure; (ii) using translation relations of Wiktionary itself.