Learning for Deep Language Understanding
Smaranda Muresan
The paper addresses the problem of learning to parse sentences to logical representations of their underlying meaning, by inducing a syntactic-semantic grammar. The approach uses a class of grammars which has been proven to be learnable from representative examples. In this paper, we introduce tractable learning algorithms for learning this class of grammars, comparing them in terms of a-priori knowledge needed by the learner, hypothesis space and algorithm complexity. We present experimental results on learning tense, aspect, modality and negation of verbal constructions.