Parsing the Dictionary of Modern Literary Russian Language with the Method of SCD Configurations. The Lexicographic Modeling

• Main Authors: Neculai Curteanu, Svetlana Cojocaru, Eugenia Burca
• Format: Article
• Language: English
• Published: Institute of Mathematics and Computer Science of the Academy of Sciences of Moldova 2012-05-01
• Series: Computer Science Journal of Moldova
• Subjects: German; French; new approach to dictionary entry parsing; the parsing method of SCD configurations; parsing the largest Romanian; German; French and Russian dictionaries; lexicographic modeling
• Online Access: http://www.math.md/files/csjm/v20-n1/v20-n1-(pp42-82).pdf

This paper extends the experience of parsing other five, sensibly different, Romanian, French, and German largest dictionaries, to \textbf{\textit{DMLRL}} (Dictionary of Modern Literary Russian Language) [18], using the optimal and portable parsing method of SCD (Segmentation-Cohesion-Dependency) configurations [7], [11], [15]. The purpose of the present paper is to elaborate the lexicographic modeling of \textbf{\textit{DMLRL}}, which necessarily precedes the sense tree parsing dictionary entries. The following \textbf{\textit{three}} SCD configurations are described: the \textbf{\textit{first one}} has to separate the lexicographic segments in a \textbf{\textit{DMLRL}} entry, the \textbf{\textit{second}} SCD-configuration concentrates on the SCD marker classes and their hypergraph hierarchy for \textbf{\textit{DMLRL}} primary and secondary senses, while the \textbf{\textit{third}} SCD configuration hands down the same modeling process to the atomic sense definitions and their examples-to-definitions. The dependency hypergraph of the third SCD configuration, interconnected to the one of the second SCD configuration, is specified completely at the atomic sense level for the first time, exceeding the SCD configuration modeling for other five dictionaries [15], [14]. Numerous examples from \textbf{\textit{DMLRL}} and comparison to \textbf{\textit{DLR-DAR}} Romanian thesaurus-dictionary support the proposed \textbf{\textit{DMLRL}} lexicographic modeling.