Document Categorization Using Graph Structuring

2018 ◽  
pp. 483-491
Author(s):  
Sandipan Sarma ◽  
Punyajoy Saha ◽  
Jaya Sil
Keyword(s):  
Document Categorization

scholarly journals The Effect of Preprocessing on Arabic Document Categorization

Algorithms ◽  
2016 ◽  
Vol 9 (2) ◽  
pp. 27 ◽  
Author(s):  
Abdullah Ayedh ◽  
Guanzheng TAN ◽  
Khaled Alwesabi ◽  
Hamdi Rajeh
Keyword(s):  
Document Categorization

Hierarchical document categorization with k-NN and concept-based thesauri

2006 ◽  
Vol 42 (2) ◽  
pp. 387-406 ◽  
Author(s):  
Sun Lee Bang ◽  
Jae Dong Yang ◽  
Hyung Jeong Yang
Keyword(s):  
Document Categorization

An Efficient Approach for Document Categorization Using Weighted Sum

2020 ◽  
pp. 686-695
Author(s):  
Vimuktha E. Salis ◽  
Ranjana S. Chakrasali ◽  
Chowdaiah Pathanjali
Keyword(s):  
Weighted Sum ◽  
Efficient Approach ◽  
Document Categorization

Semantics-Based Document Categorization Employing Semi-Supervised Learning

2015 ◽  
pp. 112-140 ◽  
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Real World ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Learning Method ◽  
Label Information ◽  
Document Categorization

The automated categorization of unstructured textual documents according to their semantic contents plays important role particularly linked with the ever growing volume of such data originating from the Internet. Having a sufficient number of labeled examples, a suitable supervised machine learning-based classifier can be trained. When no labeling is available, an unsupervised learning method can be applied, however, the missing label information often leads to worse classification results. This chapter demonstrates a method based on semi-supervised learning when a smallish set of manually labeled examples improves the categorization process in comparison with clustering, and the results are comparable with the supervised learning output. For the illustration, a real-world dataset coming from the Internet is used as the input of the supervised, unsupervised, and semi-supervised learning. The results are shown for different number of the starting labeled samples used as “seeds” to automatically label the remaining volume of unlabeled items.

Local and Global Latent Semantic Analysis for Text Categorization

2014 ◽  
Vol 4 (3) ◽  
pp. 1-13
Author(s):  
Khadoudja Ghanem
Keyword(s):  
Information Retrieval ◽  
High Precision ◽  
Latent Semantic Analysis ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Experimental Results ◽  
Semantic Approach ◽  
Document Categorization ◽  
Second Use

In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.

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