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.
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