Context-sensitive grammar

About: Context-sensitive grammar is a research topic. Over the lifetime, 1938 publications have been published within this topic receiving 45911 citations. The topic is also known as: CSG.

Learning Unification-Based Natural Language Grammars

Abstract: When parsing unrestricted language, wide-covering grammars often undergenerate. Undergeneration can be tackled either by sentence correction, or by grammar correction. This thesis concentrates upon automatic grammar correction (or machine learning of grammar) as a solution to the problem of undergeneration. Broadly speaking, grammar correction approaches can be classified as being either {\it data-driven}, or {\it model-based}. Data-driven learners use data-intensive methods to acquire grammar. They typically use grammar formalisms unsuited to the needs of practical text processing and cannot guarantee that the resulting grammar is adequate for subsequent semantic interpretation. That is, data-driven learners acquire grammars that generate strings that humans would judge to be grammatically ill-formed (they {\it overgenerate}) and fail to assign linguistically plausible parses. Model-based learners are knowledge-intensive and are reliant for success upon the completeness of a {\it model of grammaticality}. But in practice, the model will be incomplete. Given that in this thesis we deal with undergeneration by learning, we hypothesise that the combined use of data-driven and model-based learning would allow data-driven learning to compensate for model-based learning's incompleteness, whilst model-based learning would compensate for data-driven learning's unsoundness. We describe a system that we have used to test the hypothesis empirically. The system combines data-driven and model-based learning to acquire unification-based grammars that are more suitable for practical text parsing. Using the Spoken English Corpus as data, and by quantitatively measuring undergeneration, overgeneration and parse plausibility, we show that this hypothesis is correct.

Stratified boolean grammars

Abstract: We study Boolean grammars. We introduce stratified semantics for Boolean grammars. We show, how to check, if a Boolean grammar generates a language according to this semantics. We show, that stratified semantics covers a class of important and natural languages. We introduce a recognition algorithm for Boolean grammars compliant to this semantics.

Direct automated inversion of logic grammars

Abstract: Reversibility of logic grammars in natural language processing is desirable for both theoretical and practical reasons. This paper addresses this topic in describing a new approach to automated inversion of logic grammars: the Direct Inversion Approach (dia). A logic grammar is inverted by automatically altering the order of literals in the grammar and reformulating certain recursive procedures at compile time. The inversion process results in a new executable grammar, which is evaluated top-down and left-to-right (using a standard Prolog interpreter), but not left-to-right with respect to the original grammar. Thedia improves upon related approaches not only in being fully automated and computationally tractable, but also with respect to the class of grammars it is able to invert and the performance of the new executable grammar produced.

ML-Tuned Constraint Grammars

Abstract: In this paper we present a new method for machine learning-based optimization of linguist-written Constraint Grammars. The effect of rule ordering/sorting, grammarsectioning and systematic rule changes is discussed and quantitatively evaluated. The F-score improvement was 0.41 percentage points for a mature (Danish) tagging grammar, and 1.36 percentage points for a half-size grammar, translating into a 7-15% error reduction relative to the performance of the untuned grammars.

Treating Coordination with Datalog Grammars

Abstract: In previous work we studied a new type of DCGs, Datalog grammars, which are inspired on database theory. Their efficiency was shown to be better than that of their DCG counterparts under (terminating) OLDT-resolution. In this article we motivate a variant of Datalog grammars which allows us a meta-grammatical treatment of coordination. This treatment improves in some respects over previous work on coordination in logic grammars, although more research is needed for testing it in other respects.