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01.10.2019 | ARTIFICIAL INTELLIGENCE TECHNIQUES IN PATTERN RECOGNITION AND IMAGE ANALYSIS | Ausgabe 4/2019

Pattern Recognition and Image Analysis 4/2019

Estimation of the Closeness to a Semantic Pattern of a Topical Text without Construction of Periphrases

Zeitschrift:
Pattern Recognition and Image Analysis > Ausgabe 4/2019
Autoren:
D. V. Mikhaylov, G. M. Emelyanov
Wichtige Hinweise
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819040114/MediaObjects/11493_2019_6031_Fig1_HTML.gif
D.V. Mikhaylov Born 1974. Graduated from the Yaroslav-the-Wise Novgorod State University, Novgorod, in 1997. Obtained his PhD (Kandidat Nauk) and his Doctoral (Doktor Nauk) degrees in Physics and Mathematics in 2003 and 2013, respectively. From 2000 to 2007 has worked at the Department of Computer Software of Novgorod State University. Now he is a Docent of the Department of Information Technologies and Systems at the same university. Since 2002 is a member of Russian Association for Pattern Recognition and Image Analysis. Scientific interests: computational linguistics and artificial intelligence. In scientific area of Pattern Recognition and Image Analysis has 43 papers.
https://static-content.springer.com/image/art%3A10.1134%2FS1054661819040114/MediaObjects/11493_2019_6031_Fig2_HTML.gif
G.M. Emelyanov Born 1943. Graduated from the Leningrad Institute of Electrical Engineering in 1966. Obtained his PhD (Kandidat Nauk) and his Doctoral (Doktor Nauk) degrees in 1971 and 1990, respectively. From 1993 to 2003, a Dean of the Faculty of Mathematics and Computer Science at Yaroslav-the-Wise Novgorod State University. Now he is a Professor of the Department of Information Technologies and Systems at the same university. Scientific interests: construction of problem-oriented computing systems of image processing and analysis. He is the author of 98 publications in the field of pattern recognition and image analysis.
Translated by A.M. Khaitin

Abstract

The paper considers the problem of numerical estimation of the closeness of a topical text to the most rational linguistic variant (i.e. semantic pattern or sense standard) of the description of the knowledge fragment it represents without paraphrasing. This problem is relevant when implementing targeted selection of text information by the maximum of the useful semantic component with respect to the tasks solved by the user. Examples of practical applications may include selection of papers for scientific publishing and design of training courses and educational portals. In the suggested solution, the basis of the estimate of the closeness of the text to the semantic pattern is the splitting of the words of each of its phrases into classes by the TF-IDF metric value relative to texts of a corpus preformed by an expert. Abstracts of scientific papers together with their titles are analyzed. The suggested numerical estimate of closeness to the sense standard makes it possible to rank articles by the significance of the described fragments of knowledge regarding a given subject area and by non-redundancy of the description itself. Here, the semantic images of the texts closest to the semantic pattern specify the words with the highest TF-IDF values, which, when placed next to each other in the linear series of a phrase, are, most probably, semantically related and form key combinations with words whose mentioned metric is close to average. To classify word combinations as key ones, the interpretation of the TF-IDF metric, estimating the number of simultaneous occurrences of all words in the analyzed combination into phrases of the individual document, is introduced.

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