Document Categorization Based on Usage of Features Reduction with Synonyms Clustering in Weak Semantic Map

Kostkina, Anastasiya; Bodunkov, Denis; Klimov, Valentin

doi:10.1016/j.procs.2018.11.061

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Kostkina et al [13], they suggested a new approach which expanded the features of short text based Wikipedia and Word2vec. The first phase was the creation of Wikipedia's semantine related definition sets.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Devi¹,

Kumar²

2020

IJACSA

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As the size of the document collections are increasing day-by-day, finding an essential document clusters for classification problem is one of the major problem due to high inter and intra document variations. Also, most of the conventional classification models such as SVM, neural network and Bayesian models have high true negative rate and error rate for document classification process. In order to improve the computational efficacy of the traditional document classification models, a hybrid feature extraction-based document cluster approach and classification approaches are developed on the large document sets. In the proposed work, a hybrid glove feature selection model is proposed to improve the contextual similarity of the keywords in the large document corpus. In this work, a hybrid document clustering similarity index is optimized to find the essential key document clusters based on the contextual keywords. Finally, a hybrid document classification model is used to classify the clustered documents on large corpus. Experimental results are conducted on different datasets, it is noted that the proposed document clustering-based classification model has high true positive rate, accuracy and low error rate than the conventional models.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Devi¹,

Kumar²

2020

IJACSA

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“…Sunaina Kotekar Sowmya S.Kamath [7] propose a novel technique to cluster service documents into functionally similar service groups using the Cat Swarm Optimisation Algorithm. Anastasiya Kostkina, Denis Bodunkov, Valentin Klimov [8] investigate the impact of the approach on the quality of classification of documents and describe its application to the implementation of the document categorization. Mariam Thomas, Anisha, Resmipriya, M.G.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Data Mining K-Means Document Clustering using TFIDF and Word Frequency Count

Arivarasan¹,

Karthikeyan²

2019

IJRTE

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In the rapid development of www the amount of documents used increases in a rapid speed. This produces huge gigabyte of text document processing. For indexing as well as retrieving the required text document an efficient algorithms produce better performance by achieving good accuracy. The algorithms available in the field of data mining also provide a variety of new innovations regarding data mining. This increases the interest of the researchers to develop many essential models in the field of text data mining. In the proposed model is a two step text document clustering approach by K-Means algorithm. The first step includes Pre_Processing and second step includes clustering process. For Pre-Processing the method performs the tokenization approach. The distinct words are identified and the distinct words frequency of occurrence, TFIDF weights of the occurrences are calculated to form a document feature vector separately. In the clustering phase the feature vector is clustered by performing K-means algorithm by implementing various similarity measures.

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“…The work [8] describes the results of research of a proprietary approach based on the use of a semantic map as a feature reduction tool for document classification. They investigated the impact of this approach on the quality of document classification and describe its application to the implementation of document categorization.…”

Section: Introductionmentioning

confidence: 99%

Development of intelligent electronic document management system model based on machine learning methods

Sambetbayeva

Kuspanova

Yerimbetova

et al. 2022

EEJET

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With the daily increase in document flow, as well as the transition to paperless document management around the world, the demand for electronic document management systems is increasing. This significantly requires optimization of these systems in terms of quality document information retrieval and document management. However, research based on statistical methods cannot effectively handle large amounts of data extracted from electronic documents. In this regard, machine learning methods can effectively solve this problem. This paper presents an approach to building a model of an intelligent document management system using machine learning techniques to ensure efficient employee performance in organizations. The authors have solved a number of problems to optimize each of the document management subsystems, resulting in the development of an intelligent document management system model, which can be effectively applied to enterprises, government and corporate institutions. The feasibility and effectiveness of the proposed model of intelligent document management system based on machine learning and multi-agent modeling of information retrieval processes provides maximum reliability and reduced time of work on documents. The obtained results show that with the help of the presented model it is possible to further develop an intelligent document management system that will allow an electronic document to qualitatively go through the whole life cycle of a document, starting from the moment of document registration and finishing with its closing, i.e. execution, which will greatly facilitate the daily work of users with large volumes of documents. At the same time, the paper considers the application of topic modeling methods and algorithms of text analysis based on a multi-agent approach, which can be used to build an intelligent document management system.

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Document Categorization Based on Usage of Features Reduction with Synonyms Clustering in Weak Semantic Map

Cited by 7 publications

References 5 publications

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Data Mining K-Means Document Clustering using TFIDF and Word Frequency Count

Development of intelligent electronic document management system model based on machine learning methods

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