Exact Grammaticality Judgement of Likely Parse Tree

Jean-Philippe Prost (1)

1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

The robustness of probabilistic parsing generally comes at the expense of grammaticality judgementthe grammaticality of the most probable output parse remaining unknown. Parsers, such as the Stanford or the Reranking ones, can not discriminate between grammatical and ungrammatical probable parses, whether their surface realisations are themselves grammatical or not. In this paper we show that a Model-Theoretic representation of Syntax alleviates the grammaticality judgement on a parse tree. In order to demonstrate the practicality and usefulness of an alliance between stochastic parsing and knowledgebased representation, we introduce an exact method for putting a binary grammatical judgement on a probable phrase structure. We experiment with parse trees generated by a probabilistic parser. We show experimental evidence on parse trees generated by a probabilistic parser to confirm our hypothesis.

Domains: Computer Science [cs]; Computation and Language [cs.CL]; Computer Science [cs]; Computer Science [cs]; Artificial Intelligence [cs.AI]

Jean-Philippe Prost. Exact Grammaticality Judgement of Likely Parse Tree. [Research Report] Lirmm, University of Montpellier. 2014. ⟨hal-03563629⟩