Parsing Of Context-free Languages Research Articles

An innovative finite state concept for recognition and parsing of context-free languages

In the full paper in the companion volume, we introduce a new subclass of the context free languages, the meta-deterministic languages, which includes the deterministic languages, but also the languages that result if deterministic languages are combined via regular expressions.

PARSING UNCERTAIN CONTEXT-FREE LANGUAGES BY GENETIC ALGORITHMS

The parsing of context free languages can be done by efficient algorithms in O(n3) for general context-free case and in O(n) for certain classes as LL and LR. All these algorithms are very strict, they fail to recover on any insignificant input error. In this paper we propose a robust parsing technique for uncertain inputs based on genetic algorithms. As the genetic techniques are computational expensive the genetic parser is not intended to substitute classical parser, but to assist it. During the parsing process, erroneous input words could be passed to the genetic parser in order to find the best approximation of the input word. The technique is parallelizable so that in a proper computing environment the problem of computing resource crunching can be surpassed.

Parallel recognition and parsing on mesh connected computers with multiple broadcasting

In this paper we present an optimal linear time algorithm for recognition and parsing of context-free languages on n × n mesh connected computers with multiple broadcasting, for an input string of length n. Our algorithm is based on the well-known Cocke-Younger-Kasami (CYK) algorithm for the recognition and parsing of context-free languages, and is faster than two recent algorithms available in the literature. The parallel recognition of context-free languages is of particular importance since parallel compilation is an increasingly important application because of the availability of parallel hardware and the long running times of optimizing and parallelizing compilers. Our algorithm is a contribution towards this end.

Short Notes: Semantically Driven Parsing of Context-free Languages

Short Notes: Semantically Driven Parsing of Context-free Languages

Generation, recognition and parsing of context-free languages by means of recursive graphs

A device called recursive graph defining context-free languages is described. Previously such a device was used byConway [1] for a compiler design.Conway called it “transition diagram”. Roughly, a recursive graph is a finite set of finite state graphs, which are used in a recursive manner. A recursive graph covers the essential features of both standard devices: It describes the syntactical structure as grammars do, and it represents a method for recognition and parsing as push-down automata do. A notion ofk-determinacy is introduced for recursive graphs and for a restricted kind of them, called simple recursive graphs. Thek-deterministic simple recursive graphs are more general thanLL (k) grammars [5] with respect to parsing-power, but equal with respect to language generation power. The more generalk-deterministic recursive graphs cannot parse the full set ofLR (k)-grammars [4], but they can recognize the full set ofLR (k)-languages. The set of languages recognized by (simple) recursive graphs is the set of context-free languages.

Recognition and parsing of context-free languages in time n3

A recognition algorithm is exhibited whereby an arbitrary string over a given vocabulary can be tested for containment in a given context-free language. A special merit of this algorithm is that it is completed in a number of steps proportional to the “cube” of the number of symbols in the tested string. As a byproduct of the grammatical analysis, required by the recognition algorithm, one can obtain, by some additional processing not exceeding the “cube” factor of computational complexity, a parsing matrix—a complete summary of the grammatical structure of the sentence. It is also shown how, by means of a minor modification of the recognition algorithm, one can obtain an integer representing the ambiguity of the sentence, i.e., the number of distinct ways in which that sentence can be generated by the grammar. The recognition algorithm is then simulated on a Turing Machine. It is shown that this simulation likewise requires a number of steps proportional to only the “cube” of the test string length.