An approach based on analysis of n-grams on links of words to extract the knowledge and relevant linguistic means on subject-oriented text sets

Mikhaylov, D. V.; Kozlov, A.; Emelyanov, G. M.

doi:10.18287/2412-6179-2017-41-3-461-471

Cited by 13 publications

(8 citation statements)

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“…These studies demonstrate potential benefits of analysis of big data obtained from SNS [14,15]. However, they are the first steps in a long journey.…”

Section: Introductionmentioning

confidence: 83%

Application of the principal component analysis to detect semantic differences during the content analysis of social networks

Rytsarev

Козлов²,

Kravtsova

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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In this paper, we propose an approach to semantic differences detection in texts presented in the form of frequency dictionaries. The original text data has been obtained by collecting records on various online communities. We have implemented a specialized software module that allows us to analyze and download both posts and comments from the social network VK's open communities. To build our frequency dictionary, we have developed an algorithm that takes into account the peculiarities of the data collected from social networks. In the article, we propose an approach based on the use of methods reducing the dimension of feature spaces to identify keywords based on the analysis of their frequency of usage. The algorithm we present uses the principal component analysis technique. As a result, we have shown that by using the coefficients of the obtained linear transformation, it is possible to estimate the importance of words. With the help of these estimates, we were able to identify not only key words, but also semantic differences in social networks communities. The proposed approach can also be used to form metrics and calculate the social distance between Internet communities.

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“…These studies demonstrate potential benefits of analysis of big data obtained from SNS [14,15]. However, they are the first steps in a long journey.…”

Section: Introductionmentioning

confidence: 83%

Application of the principal component analysis to detect semantic differences during the content analysis of social networks

Rytsarev

Козлов²,

Kravtsova

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

show abstract

“…At present, there are a large number of approaches for finding similar strings and small texts [21][22][23][24][25]. For large texts (from 1000 words), the search for coincidences is difficult due to the large time expenditure.…”

Section: The Algorithm Of Fuzzy Duplicate Texts Detectionmentioning

confidence: 99%

The problem of fuzzy duplicate detection of large texts

Sharapova¹,

Sharapov²

2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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In the paper, considered the problem of fuzzy duplicate detection. There are given the basic approaches to detection of text duplicates-distance between strings, fuzzy search algorithms without indexing data, fuzzy search algorithms with indexing data. Thereview of existing methods for the fuzzy duplicate detection is given. The algorithm of fuzzy duplicate detection is present. The algorithm of fuzzy duplicate texts detection was implemented in the system AVTOR.NET. The paper presents the results of testing of the system.The use of filtering text, stemming and character replacement, allow the algorithm to found duplicates even in minor modified texts.

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“…At present, wiki-technologies are used to organize corporate KB. It is necessary to solve the task of knowledge extracting from wiki-resources [14,15,16,23,27]. Table 1 contains the result of mapping the KB entities to the wiki-resource entities [22].…”

Section: Extracting Knowledge From Wiki-resourcesmentioning

confidence: 99%

Development of a knowledge base based on context analysis of external information resources

Yarushkina

Filippov

Moshkin

2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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The article describes the process of developing a knowledge base (KB). The content of KB is formed as a result of the analysis of the contexts of external information resources. In this case, the context is a certain "point of view" on the problem area (PrA) and its features. A graph database (DB) Neo4j is used as the basis for storing the contents of the KB in the form of an ontology. An attempt is made to implement the mechanism of inference by the contents of a graph database. The mechanism is used to dynamically generate the screen forms of the user interface to simplify the work with the KB. This article also describes the method of extension of KB based on the content of the wiki-resources and relational databases.

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An approach based on analysis of n-grams on links of words to extract the knowledge and relevant linguistic means on subject-oriented text sets

Cited by 13 publications

References 0 publications

Application of the principal component analysis to detect semantic differences during the content analysis of social networks

Application of the principal component analysis to detect semantic differences during the content analysis of social networks

The problem of fuzzy duplicate detection of large texts

Development of a knowledge base based on context analysis of external information resources

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