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Um Modelo Estatístico Gerativo para o Aprendizado Não Supervisionado das Estruturas Argumentais dos Verbos

Thiago Alexandre Salgueiro PardoDaniel MarcuMaria das Graças Volpe Nunes

This paper presents a statistical generative model for unsupervised learning of verb argument structures. The model is based on the noisy-channel model and is trained with the Expectation-Maximization algorithm. The model was used to induce the argument structures for the 1.500 most frequent verbs in English. The evaluation of a sample of this verb set showed that about 80% of the structures were considered plausible by humans. The structures also show correct patterns of verb usage not present in PropBank, a manually developed semantic resource for verbs.

http://www.lbd.dcc.ufmg.br/colecoes/til/2005/007.pdf

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