Automatic Grammar Induction from Semantic Parsing

In this thesis, we investigate an approach for grammar induction that relies on se-mantics to drive the automatic learning of syntax rules. Specifically, we develop a semantic parser, which parses utterances based on "meaning, " rather than syntax, by combining only words and phrases that satisfy a given set of semantic constraints. We subsequently extract a syntactic grammar from the resulting semantic-level phrases, trying various approaches to generalize this grammar. We evaluate the learned gram-mar utilizing two sets of experiments, restricting our test sets to semantically valid utterances. First, we use the grammar to parse new utterances from the same do-main in which it was learned; the learned grammar covers 98 % of the utterances handled by the semantic constraints. Second, we parse utterances from a new do-main, assessing the portability of the grammar. Here, the grammar covers 85 % of the semantically valid utterances. Semantic parsing proves to be a very powerful and useful mechanism, independent of syntax, providing an utterance's "meaning repre-sentation " directly. Furthermore, our experiments illustrate that this technique ha