Implementation of Unsupervised and Supervised Learning Systems for Multilingual Text Categorization

2007 ◽  
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang
Keyword(s):  
Supervised Learning ◽  
Text Categorization ◽  
Learning Systems ◽  
Multilingual Text

scholarly journals Classifying Multilingual Text in Quality Assurance

2021 ◽  
Vol 12 (3) ◽  
pp. 1987-1992
Author(s):  
Sze Pei Tan Et.al
Keyword(s):  
Machine Learning ◽  
Life Science ◽  
Real Life ◽  
Learning Systems ◽  
Daily Activities ◽  
Multinational Company ◽  
Failure Investigation ◽  
Machine Learning Model ◽  
Multilingual Text ◽  
Life Challenge

Machine learning systems play an important role in helping and assisting engineers in their daily activities. Many jobs can now be automated, and one of them is in handling and processing customers’ complaints before they could proceed with failure investigation. In this paper, we discuss a real-life challenge faced by the manufacturing engineers in a life science multinational company. This paper presents a step by step methodology of multilingual translation and multiclassification of Repair Codes. This solution will allow manufacturing engineers to take advantage of machine learning model to reduce the time taken to manually translate row by row and verify the Repair Codes in the file.

scholarly journals Multi-label Classification: a survey

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1045 ◽  
Author(s):  
Vaishali S. Tidake ◽  
Shirish S. Sane
Keyword(s):  
Supervised Learning ◽  
Text Categorization ◽  
Learning Approach ◽  
Multimedia Annotation ◽  
Popular Approach ◽  
Label Data ◽  
Multiple Classifiers ◽  
Huge Data ◽  
Use Of Internet ◽  
Multiple Categories

Wide use of internet generates huge data which needs proper organization leading to text categorization. Earlier it was found that a document describes one category. Soon it was realized that it can describe multiple categories simultaneously. This scenario reveals the use of multi-label classification, a supervised learning approach, which assigns a predefined set of labels to an object by looking at its characteristics. Earlier used in text categorization, but soon it became the choice of researchers for wide applications like marketing, multimedia annotation, bioinformatics. Two most common approaches for multi-label classification are transformation which takes the benefit of existing single label classifiers preceded by converting multi-label data to single label, or an adaptation which designs classifiers which handle multi-label data directly. Another popular approach is ensemble of multiple classifiers taking votes of all. Other approaches are also available namely algorithm independent and algorithm dependent approach. Based on results produced, suitable metric is used for example or label wise evaluation which depends on whether prediction is binary or ranking. Every approach offers benefits and issues like loss of label dependency in transformation, complexity in case of adaptation, improvement in results using ensemble which should be considered during design of underlying application.  

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