A long-standing goal for the field of artificial intelligence is to enable computer understanding of human languages. A core requirement in reaching this goal is the ability to transform individual sentences into a form better suited for computer manipulation. This ability, semantic parsing, requires several knowledge sources, such as a grammar, lexicon, and parsing mechanism.

Building natural language parsing systems by hand is a tedious, error-prone undertaking. We build on previous research in automating the construction of such systems using machine learning techniques. The result is a combined system that learns semantic lexicons and semantic parsers from one common set of training examples. The input required is a corpus of sentence/representation pairs, where the representations are in the output format desired. A recent system, WOLFIE, learns semantic lexicons to be used as background knowledge by a previously developed parser acquisition system, CHILL. The combined system is tested on a real world domain of answering database queries. We also compare this combination to a combination of CHILL with a previously developed lexicon learner, demonstrating superior performance with our system. Finally, we show the ability of the system to learn to process natural languages other than English.