We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars.We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.
We describe four different parsing algorithms for Linear Context-Free Rewriting Systems (Vijay-Shanker et al., 1987). The algorithms are described as deduction systems, and possible optimizations are discussed.The only parsing algorithms presented for linear contextfree rewriting systems (LCFRS; Vijay-Shanker et al., 1987) and the equivalent formalism multiple context-free grammar (MCFG;Seki et al., 1991) are extensions of the CKY algorithm (Younger, 1967), more designed for their theoretical interest, and not for practical purposes. The reason for this could be that there are not many implementations of these grammar formalisms. However, since a very important subclass of the Grammatical Framework (Ranta, 2004) is equivalent to LCFRS/MCFG (Ljunglöf, 2004a;Ljunglöf, 2004b), there is a need for practical parsing algorithms.In this paper we describe four different parsing algorithms for Linear Context-Free Rewriting Systems. The algorithms are described as deduction systems, and possible optimizations are discussed. Introductory definitionsA record is a structure Γ = {r 1 = a 1 ; . . . ; r n = a n }, where all r i are distinct. That this can be seen as a set of feature-value pairs. This means that we can define a simple version of record unification Γ 1 Γ 2 as the union Γ 1 ∪Γ 2 , provided that there is no r such that Γ 1 .r = Γ 2 .r.We sometimes denote a sequence X 1 , . . . , X n by the more compact X. To update the ith record in a list of records, we write Γ[i := Γ]. To substitute a variable B k for a record Γ k in any data structure Γ, we write Γ[B k /Γ k ].
MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.
We present an algorithm for converting Grammatical Framework grammars (Ranta, 2004) into the Regulus unification-based framework (Rayner et al., 2006). The main purpose is to take advantage of the Regulusto-Nuance compiler for generating optimized speech recognition grammars. But there is also a theoretical interest in knowing how similar the two grammar formalisms are.
This paper accompanies a demo of the GoDiS system. Work on~hi~ system was reported at IJCAI-99 (Bohlin et-al.~ 1999). GoDiS is a prototype dialogue system for information-seeking dialogue, capable of accommodating questions and tasks to enable the user to present information in any desired order, without explicitly naming the dialogue task. GoDiS is implemented using the TRINDIKIT software package, which enables implementation of these behaviours in a compact and natural way.
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