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.

Modular Grammar Engineering in GF

Abstract: The Grammatical Framework GF is a grammar formalism designed for multilingual grammars. A multilingual grammar has a shared representation, called abstract syntax, and a set of concrete syntaxes that map the abstract syntax to different languages. A GF grammar consists of modules, which can share code through inheritance, but which can also hide information to achieve division of labour between grammarians working on different modules. The goal is to make it possible for linguistically untrained programmers to write linguistically correct application grammars encoding the semantics of special domains. Such programmers can rely on resource grammars, written by linguists, which play the role of standard libraries. Application grammarians use resource grammars through abstract interfaces, and the type system of GF guarantees that grammaticality is preserved. The ongoing GF resource grammar project provides resource grammars for ten languages. In addition to their use as libraries, resource grammars serve as an experiment showing how much grammar code can be shared between different languages.

Probabilistic Grammars and their Applications

Abstract: Formal grammars are widely used in speech recognition, language translation, and language understanding systems. Grammars rich enough to accommodate natural language generate multiple interpretations of typical sentences. These ambiguities are a fundamental challenge to practical application. Grammars can be equipped with probability distributions, and the various parameters of these distributions can be estimated from data (e.g., acoustic representations of spoken words or a corpus of hand-parsed sentences). The resulting probabilistic grammars help to interpret spoken or written language unambiguously. This article reviews the main classes of probabilistic grammars and points to some active areas of research.

Dynamic Compilation of Weighted Context-Free Grammars

Abstract: Weighted context-free grammars are a convenient formalism for representing grammatical constructions and their likelihoods in a variety of language-processing applications. In particular, speech understanding applications require appropriate grammars both to constrain speech recognition and to help extract the meaning of utterances. In many of those applications, the actual languages described are regular, but context-free representations are much more concise and easier to create. We describe an efficient algorithm for compiling into weighted finite automata an interesting class of weighted context-free grammars that represent regular languages. The resulting automata can then be combined with other speech recognition components. Our method allows the recognizer to dynamically activate or deactivate grammar rules and substitute a new regular language for some terminal symbols, depending on previously recognized inputs, all without recompilation. We also report experimental results showing the practicality of the approach.

Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm

Abstract: This paper describes a method of synthesizing context free grammars from positive and negative sample strings, which is implemented in a grammatical inference system called Synapse. The method is based on incremental learning for positive samples and a rule generation method by “inductive CYK algorithm,” which generates minimal production rules required for parsing positive samples. Synapse can generate unambiguous grammars as well as ambiguous grammars. Some experiments showed that Synapse can synthesize several simple context free grammars in considerably short time.