Comunicació presentada a: COLING 2014, the 25th International Conference on Computational Linguistics, celebrat a Dublin (Irlanda), del 23 al 29 d'agost de 2014.

The work presented here addresses the use of unmarked contexts in pattern-based nominal lexical semantic classification. We define unmarked contexts to be the counterposition of the class-indicatory, or marked, contexts. Its aim is to evaluate how unmarked contexts can be used to improve the accuracy and reliability of lexical semantic classifiers. Results demonstrate that the combined use of both types of distributional information (marked and unmarked) is crucial to improve classification. This result was replicated using two different corpora, demonstrating the robustness of the method proposed.

This work was funded with the support of the SUR of the DEC of the Generalitat de Catalunya and the European Social Fund, by SKATER TIN2012-38584-C06-05, and by Fundação para a Ciência e a Tecnologia (FCT) post-doctoral fellowship SFRH/BPD/79900/2011.