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Towards robustness in parsing  fuzzifying contextfree language recognition
 Developments in Language Theory II  At the Crossroad of Mathematics, Computer Science and Biology
, 1996
"... We discuss the concept of robustness with respect to parsing or recognizing a contextfree language. Our approach is based on the notions of fuzzy language, (generalized) fuzzy contextfree grammar, and parser/recognizer for fuzzy languages. As concrete examples we consider a robust version of Cocke– ..."
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We discuss the concept of robustness with respect to parsing or recognizing a contextfree language. Our approach is based on the notions of fuzzy language, (generalized) fuzzy contextfree grammar, and parser/recognizer for fuzzy languages. As concrete examples we consider a robust version of Cocke–Younger–Kasami’s algorithm and a robust kind of recursive descent recognizer.
A fuzzy approach to erroneous inputs in contextfree language recognition
 Dept. of Comp. Sci., Twente University of Technology
, 1995
"... Abstract − Using fuzzy contextfree grammars one can easily describe a finite number of ways to derive incorrect strings together with their degree of correctness. However, in general there is an infinite number of ways to perform a certain task wrongly. In this paper we introduce a generalization o ..."
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Abstract − Using fuzzy contextfree grammars one can easily describe a finite number of ways to derive incorrect strings together with their degree of correctness. However, in general there is an infinite number of ways to perform a certain task wrongly. In this paper we introduce a generalization of fuzzy contextfree grammars, the socalled fuzzy contextfree Kgrammars, to model the situation of making a finite choice out of an infinity of possible grammatical errors during each contextfree derivation step. Under minor assumptions on the parameter K this model happens to be a very general framework to describe correctly as well as erroneously derived sentences by a single generating mechanism. Our first result characterizes the generating capacity of these fuzzy contextfree Kgrammars. As consequences we obtain: (i) bounds on modeling grammatical errors within the framework of fuzzy contextfree grammars, and (ii) the fact that the family of languages generated by fuzzy contextfree Kgrammars shares closure properties very similar to those of the family of ordinary contextfree languages. The second part of the paper is devoted to a few algorithms to recognize fuzzy contextfree languages: viz. a variant of a functional version of Cocke−Younger− Kasami’s algorithm and some recursive descent algorithms. These algorithms turn out to be robust in some very elementary sense and they can easily be extended to corresponding parsing algorithms. 1.
Fuzzy contextfree languages — Part 2: Recognition and parsing algorithms
 Theor. Comp. Sci
, 2005
"... In a companion paper [9] we used fuzzy contextfree grammars in order to model grammatical errors resulting in erroneous inputs for robust recognizing and parsing algorithms for fuzzy contextfree languages. In particular, this approach enables us to distinguish between small errors (“tiny mistakes” ..."
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In a companion paper [9] we used fuzzy contextfree grammars in order to model grammatical errors resulting in erroneous inputs for robust recognizing and parsing algorithms for fuzzy contextfree languages. In particular, this approach enables us to distinguish between small errors (“tiny mistakes”) and big errors (“capital blunders”). In this paper we present some algorithms to recognize fuzzy contextfree languages: particularly, a modification of Cocke–Younger–Kasami’s algorithm and some recursive descent algorithms. Then we extend these recognition algorithms to corresponding parsing algorithms for fuzzy contextfree languages. These parsing algorithms happen to be robust in some very elementary sense. Key words: formal language, fuzzy contextfree grammar, grammatical error, recognition algorithm, parsing algorithm, robust parsing. 1