EVALUATION OF SEMANTIC SIMILARITY FOR SENTENCES IN NATURAL LANGUAGE BY MATHEMATICAL STATISTICS METHODS

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ID: 96778
2016
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Abstract
Subject of Research. The paper is focused on Wiktionary articles structural organization in the aspect of its usage as the base for semantic network. Wiktionary community references, article templates and articles markup features are analyzed. The problem of numerical estimation for semantic similarity of structural elements in Wiktionary articles is considered. Analysis of existing software for semantic similarity estimation of such elements is carried out; algorithms of their functioning are studied; their advantages and disadvantages are shown. Methods. Mathematical statistics methods were used to analyze Wiktionary articles markup features. The method of semantic similarity computing based on statistics data for compared structural elements was proposed.Main Results. We have concluded that there is no possibility for direct use of Wiktionary articles as the source for semantic network. We have proposed to find hidden similarity between article elements, and for that purpose we have developed the algorithm for calculation of confidence coefficients proving that each pair of sentences is semantically near. The research of quantitative and qualitative characteristics for the developed algorithm has shown its major performance advantage over the other existing solutions in the presence of insignificantly higher error rate. Practical Relevance. The resulting algorithm may be useful in developing tools for automatic Wiktionary articles parsing. The developed method could be used in computing of semantic similarity for short text fragments in natural language in case of algorithm performance requirements are higher than its accuracy specifications.
Reference Key
pismak2016evaluationnaucnotehniceskij Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pismak, A. E.;Kharitonova, A. E.;Tsopa, E. A.;Klimenkov, S. V.;
Journal naučno-tehničeskij vestnik informacionnyh tehnologij, mehaniki i optiki
Year 2016
DOI
DOI not found
URL
http://ntv.ifmo.ru/file/article/15194.pdf
Keywords
Medicine Technology (General) Engineering (General). Civil engineering (General) Technology Science optics. light psychology philology. linguistics language and literature applied optics. photonics computer engineering. computer hardware mathematics systems engineering electronic computers. computer science information theory semantic similarity computer science

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