Abstract
The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.
Highlights
Semantic parsers map sentences onto logical forms that can be used to query databases (Zettlemoyer and Collins, 2005; Wong and Mooney, 2006), instruct robots (Chen and Mooney, 2011), extract information (Krishnamurthy and Mitchell, 2012), or describe visual scenes (Matuszek et al, 2012)
Motivated by the desire to carry these advances over to semantic parsing tasks, we present a robust method for mapping dependency trees to logical forms that represent underlying predicate-argument structures
When using the standard Freebase API (“FB API”) for entity resolution, the performance of their model drops to 48.4% F1
Summary
Semantic parsers map sentences onto logical forms that can be used to query databases (Zettlemoyer and Collins, 2005; Wong and Mooney, 2006), instruct robots (Chen and Mooney, 2011), extract information (Krishnamurthy and Mitchell, 2012), or describe visual scenes (Matuszek et al, 2012). Current systems accomplish this by learning task-specific grammars (Berant et al, 2013), by using strongly-typed CCG grammars (Reddy et al, 2014), or by eschewing the use of a grammar entirely (Yih et al, 2015). (nsubj (dobj acquired Pixar) Disney) (b) The s-expression for the dependency tree. Since our approach uses dependency trees as input, we hypothesize that it will generalize better to domains that are well covered by dependency parsers than methods that induce semantic grammars from scratch
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