Formation of categories in document classification systems

1991 ◽  
pp. 91-97 ◽  
Author(s):  
Sanjiv K. Bhatia ◽  
Jitender S. Deogun ◽  
Vijay V. Raghavan
Keyword(s):  
Document Classification ◽  
Classification Systems

Advanced Applications on Bilingual Document Analysis and Processing Systems

2020 ◽  
Vol 11 (4) ◽  
pp. 149-193
Author(s):  
Shalini Puri ◽  
Satya Prakash Singh
Keyword(s):  
Feature Extraction ◽  
Real Life ◽  
Document Analysis ◽  
Document Classification ◽  
Classification Systems ◽  
Post Processing ◽  
Extraction Techniques ◽  
Dual Class ◽  
Advanced Applications ◽  
Real World Problems

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

Automatic Subject Indexing of Text

2019 ◽  
Vol 46 (2) ◽  
pp. 104-121 ◽  
Author(s):  
Koraljka Golub
Keyword(s):  
Text Categorization ◽  
Document Clustering ◽  
Document Classification ◽  
Classification Systems ◽  
Learning Approach ◽  
Advantages And Disadvantages ◽  
Knowledge Organization Systems ◽  
Machine Learning Approach ◽  
Index Terms ◽  
Subject Indexing

Automatic subject indexing addresses problems of scale and sustainability and can be at the same time used to enrich existing metadata records, establish more connections across and between resources from various metadata and resource collections, and enhance consistency of the metadata. In this work, automatic subject indexing focuses on assigning index terms or classes from established knowledge organization systems (KOSs) for subject indexing like thesauri, subject headings systems and classification systems. The following major approaches are discussed, in terms of their similarities and differences, advantages and disadvantages for automatic assigned indexing from KOSs: “text categorization,” “document clustering,” and “document classification.” Text categorization is perhaps the most widespread, machine-learning approach with what seems generally good reported performance. Document clustering automatically both creates groups of related documents and extracts names of subjects depicting the group at hand. Document classification re-uses the intellectual effort invested into creating a KOS for subject indexing and even simple string-matching algorithms have been reported to achieve good results, because one concept can be described using a number of different terms, including equivalent, related, narrower and broader terms. Finally, applicability of automatic subject indexing to operative information systems and challenges of evaluation are outlined, suggesting the need for more research.

Advanced Applications on Bilingual Document Analysis and Processing Systems

2022 ◽  
pp. 625-674
Author(s):  
Shalini Puri ◽  
Satya Prakash Singh
Keyword(s):  
Feature Extraction ◽  
Real World ◽  
Classification System ◽  
Real Life ◽  
Document Analysis ◽  
Document Classification ◽  
Classification Systems ◽  
Post Processing ◽  
Dual Class ◽  
Advanced Applications

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

scholarly journals A New Similarity Measure for Document Classification and Text Mining

2020 ◽  
Author(s):  
Mete Eminağaoğlu ◽  
Yılmaz Gökşen
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Text Mining ◽  
Similarity Measure ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Content Management ◽  
Document Classification ◽  
Classification Systems ◽  
Textual Data

Accurate, efficient and fast processing of textual data and classification of electronic documents have become an important key factor in knowledge management and related businesses in today’s world. Text mining, information retrieval, and document classification systems have a strong positive impact on digital libraries and electronic content management, e-marketing, electronic archives, customer relationship management, decision support systems, copyright infringement, and plagiarism detection, which strictly affect economics, businesses, and organizations. In this study, we propose a new similarity measure that can be used with k-nearest neighbors (k-NN) and Rocchio algorithms, which are some of the well-known algorithms for document classification, information retrieval, and some other text mining purposes. We have tested our novel similarity measure with some structured textual data sets and we have compared the results with some other standard distance metrics and similarity measures such as Cosine similarity, Euclidean distance, and Pearson correlation coefficient. We have obtained some promising results, which show that this proposed similarity measure could be alternatively used within all suitable algorithms, methods, and models for text mining, document classification, and relevant knowledge management systems. Keywords: text mining, document classification, similarity measures, k-NN, Rocchio algorithm

Scaling assumptions underlying weighting in job-classification systems.

1967 ◽  
Vol 51 (5, Pt.1) ◽  
pp. 442-443
Author(s):  
Frank J. Landy ◽  
Arthur J. Elbert
Keyword(s):  
Classification Systems ◽  
Job Classification
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