Learning Verbal Transitivity Using LogLinear Models
نویسندگان
چکیده
Portuguese dictionaries lack information about the kind of phrases or clauses a verb goes with. In this paper we describe how this information can be computationally learned from an automatically tagged corpus with almost 10,000,000 words. Loglinear modeling for categorical data will be used to analyze and automatically identify the subcatego-rization dependencies. Loglinear models were also used in an unsuper-vised clustering algorithm to accurately determine the verbal transitive-ness behavior of 196 Portuguese verbs. Evaluation of the obtained results connrmed a precision measure of 96.07% precision and a recall of 68.81% over a standard portuguese dictionary.
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