We consider the problem of learning fac- tored probabilistic CCG grammars for seman- tic parsing from data containing sentences paired with logical-form meaning representa- tions. Traditional CCG lexicons list lexical items that pair words and phrases with syntac- tic and semantic content. Such lexicons can be inefficient when words appear repeatedly with closely related lexical content. In this paper, we introduce factored lexicons, which include both lexemes to model word meaning and templates to model systematic variation in word usage. We also present an algorithm for learning factored CCG lexicons, along with a probabilistic parse-selection model. Evalua- tions on benchmark datasets demonstrate that the approach learns highly accurate parsers, whose generalization performance benefits greatly from the lexical factoring.
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