Distributional thesauri are useful in many tasks of Natural Language Processing. In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval (IR) concepts. Two main contributions are proposed. First, following the work of [8], we show how IR tools and concepts can be used with success to build a thesaurus. Through several experiments and by evaluating directly the results with reference lexicons, we show that some IR models outperform state-of-the-art systems. Secondly, we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validates the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences bring these evaluation habits into question.
@InProceedings{CLAVEAU16.931,
author = {Vincent Claveau and Ewa Kijak}, title = {Distributional Thesauri for Information Retrieval and vice versa}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {may}, date = {23-28}, location = {Portorož, Slovenia}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {978-2-9517408-9-1}, language = {english} }
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