Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

We consider the problem of learning to parse sentences to lambda-calculus representations of their underlying semantics and present an algorithm that learns a weighted combinatory categorial grammar (CCG). A key idea is to introduce non-standard CCG combinators that relax certain parts of the grammar—for example allowing flexible word order, or insertion of lexical items— with learned costs. We also present a new, online algorithm for inducing a weighted CCG. Results for the approach on ATIS data show 86% F-measure in recovering fully correct semantic analyses and 95.9% F-measure by a partial-match criterion, a more than 5% improvement over the 90.3% partial-match figure reported by He and Young (2006).

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Luke S. Zettlemoyer, Michael Collins
• Keywords: Combinatory Categorial Grammar, EMNLP 2007, Natural Language Processing, Non-standard Ccg Combinators, Weighted Ccg