Combining Supertagging and Lexicalized Tree-Adjoining Grammar Parsing∗

In this paper we study various reasons and mechanisms for combining Supertagging with Lexicalized Tree-Adjoining Grammar (LTAG) parsing. Because of the highly lexicalized nature of the LTAG formalism, we experimentally show that notions other than sentence length play a factor in observed parse times. In particular, syntactic lexical ambiguity and sentence complexity (both are terms we define in this paper) are the dominant factors that affect parsing efficiency. We show how a Supertagger can be used to drastically reduce the syntactic lexical ambiguity for a given input and can be used in combination with an LTAG parser to radically improve parsing efficiency. We then turn our attention to from parsing efficiency to parsing accuracy and provide a method by which we can effectively combine the output of a Supertagger and a statistical LTAG parser using a co-training algorithm for bootstrapping new labeled data. This combination method can be used to incorporate new labeled data from raw text to improve parsing accuracy.