Authors:
Nadia Shakir
1
;
Erum Iftikhar
2
and
Imran Sarwar Bajwa
2
Affiliations:
1
Quaid-i-Azam University, Pakistan
;
2
The Islamia University of Bahawalpur, Pakistan
Keyword(s):
Machine Learning, Topic Spotting, Decision Tree, Neural Networks, K-Nearest Neighbours, Naive Bayes.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Automatically choosing topics for text documents that describe the document contents, is a useful technique for text categorization. For example queries sent on the web can use this technique to identify the query topic and accordingly forward query to small group of people. Similarly online blogs can be categorized according to the topics they are related to. In this paper we applied machine learning techniques to the problem of topic spotting. We used supervised learning techniques which are highly dependent on training data and the particular training algorithm used. Our approach differs from automatic text clustering which uses unsupervised learning for clustering the text. Secondly the topics are known in advance and come from an exhaustive list of words. The machine learning techniques we applied are 1) neural network., 2) Naïve Bayes Classifier, 3) Instance based learning using k-nearest neighbours and 4) Decision Tree method. We used Reuters-21578 text categorization dataset
for our experiments.
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