Generalization of a Suffix Tree for RNA Structural Pattern Matching

With the emergence of broad-coverage parsers, quantitative evaluation of parsers becomes increasingly more important. We propose a dependency-based method for evaluating broad-coverage parsers that offers more meaningful performance measures than previous approaches. We also present a structural pattern-matching mechanism that can be used to eliminate inconsequential differences among different parse trees. Previous evaluation methods have only evaluated the overall performance of parsers. The dependency-based method can also evaluate parsers with respect to different kinds of grammatical relationships or different types of lexical categories. An algorithm for transforming constituency trees into dependency trees is presented, which makes the evaluation method applicable to both constituency grammars and dependency grammars.

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Supporting Conceptual Model Analysis Using Semantic Standardization and Structural Pattern Matching

Semantic Technologies for Business and Information Systems Engineering ◽

10.4018/978-1-60960-126-3.ch007 ◽

2011 ◽

pp. 125-149 ◽

Cited By ~ 1

Author(s):

Patrick Delfmann ◽

Sebastian Herwig ◽

Lukasz Lis ◽

Jörg Becker

Keyword(s):

Pattern Matching ◽

Model Comparison ◽

Conceptual Modeling ◽

Model Analysis ◽

Model Integration ◽

Generic Model ◽

Modeling Language ◽

Structural Pattern ◽

Application Problem ◽

Specific Modeling

Analysis of conceptual models is useful for a number of purposes, such as revealing syntactical errors, model comparison, model integration, and identification of business process improvement potentials, with both the model structure and the model contents having to be considered. In this contribution, we introduce a generic model analysis approach. Unlike existing approaches, we do not focus on a certain application problem or a specific modeling language. Instead, our approach is generic, making it applicable for any analysis purpose and any graph-based conceptual modeling language. The approach integrates pattern matching for structural analysis and linguistic standardization enabling an unambiguous analysis of the models’ contents.

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Multi-pattern matching with variable-length wildcards using suffix tree

Pattern Analysis and Applications ◽

10.1007/s10044-018-0733-0 ◽

2018 ◽

Vol 21 (4) ◽

pp. 1151-1165 ◽

Cited By ~ 4

Author(s):

Na Liu ◽

Fei Xie ◽

Xindong Wu

Keyword(s):

Pattern Matching ◽

Suffix Tree ◽

Variable Length

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Suffix Tree Based VF-Coding for Compressed Pattern Matching

2009 Data Compression Conference ◽

10.1109/dcc.2009.58 ◽

2009 ◽

Cited By ~ 8

Author(s):

Takuya Kida

Keyword(s):

Pattern Matching ◽

Suffix Tree

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A SYNTACTIC OMNI-FONT CHARACTER RECOGNITION SYSTEM

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001487000217 ◽

1987 ◽

Vol 01 (03n04) ◽

pp. 303-322 ◽

Cited By ~ 2

Author(s):

GEORGE WOLBERG

Keyword(s):

Pattern Matching ◽

Character Recognition ◽

Topological Structure ◽

Scale Invariance ◽

Recognition System ◽

Description Language ◽

Structural Pattern ◽

Wide Range ◽

Pattern Description ◽

Orthogonal Set

This paper introduces a syntactic omni-font character recognition system. The "omni-font" attribute reflects the wide range of fonts that fall within the class of characters that can be recognized. This includes hand-printed characters as well. A structural pattern-matching approach is employed. Essentially, a set of loosely constrained rules specify pattern components and their interrelationships. The robustness of the system is derived from the orthogonal set of pattern descriptors, location functions, and the manner in which they are combined to exploit the topological structure of characters. By virtue of the new pattern description language, PDL, developed in this paper, the user may easily write rules to define new patterns for the system to recognize. The system also features scale-invariance and user-definable sensitivity to tilt orientation.

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