scholarly journals The concept of forecasting based on semantic analysis of text information

2020 ◽  
Vol 1703 ◽  
pp. 012044
Author(s):  
A I Ivanus ◽  
V P Nevezhin ◽  
A A Pustynkin ◽  
I S Vladimirov ◽  
E I Chernova
Keyword(s):  
Semantic Analysis ◽  
Text Information

scholarly journals Semantic Analysis Models and Methods of the Text Information in the Building Normative Base

2020 ◽  
Vol 9 (4) ◽  
pp. 1873-1879
Keyword(s):  
Building Materials ◽  
Semantic Analysis ◽  
The European Union ◽  
Technical Regulation ◽  
Text Documents ◽  
Workflow Systems ◽  
Normative Documents ◽  
Text Information ◽  
Normative Documentation ◽  
Analysis System

The research is aimed at overcoming the uncertainties associated with the reform of legislation regarding the technical regulation of the building industry. The main goal of the paper is to systematize the normative base of building of Ukraine in the conditions of adaptation of normative documents to the requirements of the European Union. In order to achieve this goal, an analysis of the peculiarities of the information resource of the normative base was conducted and the nature of the uncertainty contained in the normative documentation of the industry was investigated. The status of modern electronic databases of normative documentation in the building and building materials industry of Ukraine is described. The scheme for identifying different documents on the same issue is proposed. The possibility of applying models and methods of fuzzy mathematics to formalization of texts of building norms and rules and expression of their semantics in the internal language of the Semantic Text Information Analysis System is shown. The practical value of the paper is seen in the improvement of the organizational and technical system "Technical Regulation in Building" by minimizing the number of regulations on the same issue and achieving consistency between normative. The paper's scientific novelty of is to apply the model of artificial intelligence to the solution of the problem of semantic analysis of documents contained in electronic bases of normative documentation of the system of technical regulation in building. The capability of artificial neural networks of the ImegNet category to solve the problem of semantic analysis text documents of Building Normative Base is proved Further researches will focus on forming the Text Information Semantic Analysis System knowledge base and adapting the Deep Structured Semantic Model to the task of assessing the degree of closeness between a Semantic Request and a Semantic Normative Document. The results can be used to improve other workflow systems.

Latent Semantic Analysis and Beyond

2010 ◽  
pp. 546-570 ◽  
Author(s):  
Anne Kao
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Search Space ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
Mathematical Approach ◽  
Text Information ◽  
Space Method

Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization.

Latent Semantic Analysis for Text Mining and Beyond

2013 ◽  
pp. 253-280 ◽  
Author(s):  
Anne Kao ◽  
Steve Poteet ◽  
Jason Wu ◽  
William Ferng ◽  
Rod Tjoelker ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Latent Semantic Analysis ◽  
Web Mining ◽  
Semantic Analysis ◽  
Search Space ◽  
Latent Semantic Indexing ◽  
Cross Language Information Retrieval ◽  
Text Information ◽  
Cross Language

Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization. While the major application of LSA is for text mining, it is also highly applicable to cross-language information retrieval, Web mining, and analysis of text transcribed from speech and textual information in video.

STRUCTURE OF A SOFTWARE PRODUCT FOR SEMANTIC ANALYSIS OF TEXTUAL INFORMATION

2021 ◽  
Vol 53 (1) ◽  
pp. 9-17
Author(s):  
ISKANDAR M. AZHMUKHAMEDOV ◽  
◽  
KIRILL A. ZORIN ◽  
VALENTINA YU. KUZNETSOVA ◽  
◽  
...  
Keyword(s):  
Web Application ◽  
Semantic Analysis ◽  
Textual Information ◽  
Semantic Categories ◽  
Information Monitoring ◽  
Marketing Social ◽  
General Logic ◽  
Software Product ◽  
Text Information

The article is devoted to the development of software for semantic text analysis. Describes the General logic of the developed web application, which implements the classification of textual information by previously predefined semantic categories. This article provides usage and deployment diagrams, as well as a General diagram of the algorithm. The methods implemented in the app allow you to increase the speed of processing text information. The use of the developed software product allows you to quickly process text information of any content, as well as conduct a deeper semantic analysis of the text. The implemented method is relevant in many areas of activity where you have to work with text information: monitoring of emotional mood towards the company, marketing, social network analysis.

Feel Thine Own Self – Mood Congruency Evaluation of Emotional State Adjectives

2020 ◽  
pp. 1-17
Author(s):  
Szczepan J. Grzybowski ◽  
Miroslaw Wyczesany ◽  
Jan Kaiser
Keyword(s):  
Mood Induction ◽  
Semantic Analysis ◽  
Emotional State ◽  
Time Window ◽  
Event Related Potential ◽  
Negativity Bias ◽  
Positive Potential ◽  
Semantic Access ◽  
Current Mood ◽  
Component Time

Abstract. The goal of the study was to explore event-related potential (ERP) differences during the processing of emotional adjectives that were evaluated as congruent or incongruent with the current mood. We hypothesized that the first effects of congruence evaluation would be evidenced during the earliest stages of semantic analysis. Sixty mood adjectives were presented separately for 1,000 ms each during two sessions of mood induction. After each presentation, participants evaluated to what extent the word described their mood. The results pointed to incongruence marking of adjective’s meaning with current mood during early attention orientation and semantic access stages (the P150 component time window). This was followed by enhanced processing of congruent words at later stages. As a secondary goal the study also explored word valence effects and their relation to congruence evaluation. In this regard, no significant effects were observed on the ERPs; however, a negativity bias (enhanced responses to negative adjectives) was noted on the behavioral data (RTs), which could correspond to the small differences traced on the late positive potential.

Thinking Like a Teacher Enhances Memory for Text Information

2012 ◽  
Author(s):  
John F. Nestojko ◽  
Dung C. Bui ◽  
Henry L. Roediger ◽  
Sandra Hale
Keyword(s):  
Memory For Text ◽  
Text Information
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