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Explains need of classification,methods used and its application
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Text Classification/Categorization
1.
NLP For Text Categorization/Classification Domain-Natural
Language Processing Prepared By-Abhishek Oswal Guide-Jayshree Ghorpade
2.
Some Questions What is
NLP Detecting ->Patterns Features Models What is Cassification What is Text Classification Why Text Classification
3.
Promblem Type •Supervised •You know
about it •Train data •Fruits Analogy •Unsupervised •You don't know about it •Untrain data
4.
Supervised •Regression and Classification •Regression
-> Real estate market predict price , •Price Continious Output •Classification ->Whether it sells for more or less than asked price,discrete output
5.
Process of Classification •Data
preprocessing •Training and Test set •Creation of model •Algorithm •Classify
6.
Methods To Represent •Document
-term Matrix •Bags of words
7.
Methods To Classify •Using
Probability •Naive Bayes •Using Graphs •Simple Vector Machine •Tree •Decision Tree
8.
Naive Bayes •Predictice Model •Conditional
Probability
9.
Naive Bayes •Independent Features •Prior •Likelihood
10.
Naive Bayes
11.
Naive Bayes
12.
Another Method
13.
Simple Vector Machine
14.
SVM lOptimal plane
15.
SVM •Using margin •Marging is
no man's land
16.
SVM •Optimal plane would
be one with Biggest margin •Equation of hyperplane
17.
Applications •Email Classification •Spam Filtering •News
Organization •Classification of documents based on language •Opining Mining •Eg. •Sakaal Classifieds •Gmail Spam Mail Detection
18.
Example
19.
Comparision Naive Bayes Easy Fast Different Classes SVM Difficult Slow
(traning time) Binary Output
20.
Questions • My Questions
First •Classify my presentation •Class ->Good /Bad
21.
Fei Yu ,Jiyao
An and Hong Li,Mialiang Zhu and Ouyang Yang,”Intelligence Text Categorization Based on Bayes Algorithm”,Proceedings of International Conference on Information Acquisition.
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