scholarly journals A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

2022 ◽  
Vol 16 ◽  
pp. 23-27
Author(s):  
Mehrnoosh Bazrafkan
Keyword(s):  
Pattern Recognition ◽  
Data Structure ◽  
Time Complexity ◽  
Theoretic Approach ◽  
Top Down ◽  
Syntactic Pattern Recognition ◽  
Context Free Grammar ◽  
Linked List ◽  
Syntactic Pattern ◽  
Context Free

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

GROWTH TRANSFORMATIONS FOR PROBABILISTIC FUNCTIONS OF STOCHASTIC GRAMMARS

1996 ◽  
Vol 10 (03) ◽  
pp. 183-201 ◽  
Author(s):  
FRANCISCO CASACUBERTA
Keyword(s):  
Pattern Recognition ◽  
Continuous Extension ◽  
Syntactic Pattern Recognition ◽  
Estimation Algorithms ◽  
Syntactic Pattern ◽  
Training Samples ◽  
Finite Set ◽  
Stochastic Grammars ◽  
Context Free ◽  
Context Free Grammars

Stochastic Grammars are the most usual models in Syntactic Pattern Recognition. Both components of a Stochastic Grammar, the characteristic grammar and the probabilities attached to the rules, can be learnt automatically from training samples. In this paper, first a review of some algorithms are presented to infer the probabilistic component of Stochastic Regular and Context-Free Grammars under the framework of the Growth Transformations. On the other hand, with Stochastic Grammars, the patterns must be represented as strings over a finite set of symbols. However, the most natural representation in many Syntactic Pattern Recognition applications (i.e. speech) is as sequences of vectors from a feature vector space, that is, a continuous representation. Therefore, to obtain a discrete representation of the patterns, some quantization errors are introduced in the representation process. To avoid this drawback, a formal presentation of a semi-continuous extension of the Stochastic Regular and Context-Free Grammars is studied and probabilistic estimation algorithms are developed in this paper. In this extension, sequences of vectors, instead of strings of symbols, can be processed with Stochastic Grammars.

APPLICATION OF GUARDED FUZZY-ATTRIBUTE CONTEXT FREE GRAMMAR TO SYNTACTIC PATTERN RECOGNITION

1992 ◽  
Vol 06 (05) ◽  
pp. 777-797 ◽  
Author(s):  
KWOK-PING CHAN
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Concurrent Programming ◽  
High Dimensional ◽  
Simple Scheme ◽  
Simple Modification ◽  
Context Free Grammar ◽  
Syntactic Pattern ◽  
On Line ◽  
Context Free

High-dimensional grammars such as web grammars and plex grammars were used in syntactic recognition of complex 2-D or 3-D objects. In this paper, we present a simple modification, borrowing the concept of guards from concurrent programming to attributed grammar proposed by D. E. Knuth. We show that the resultant grammar can handle patterns described by the high-dimensional grammars. The only problem is that we may not have a simple ordering of the terminal symbols or pattern primitives. In some applications, such as on-line character recognition, the problem does not exist and hence presents a good candidate for the application. We also discuss the incorporation of fuzzy attributes and the necessary modification is hence introduced. Finally, the error transformations proposed by K. S. Fu can easily be taken into consideration and a powerful yet simple scheme presented.

Sign in / Sign up

Export Citation Format

Share Document