( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
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Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka
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Agenda
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1. What is Natural Language Processing?
2. NLP Components
3. Stemming
4. Lemmatization
5. Applications of Stemming & Lemmatization
6. The differences between the Two
5. What is Text Mining ?
Text Mining / Text Analytics is the process of deriving
meaningful information from natural language text
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6. Text Mining and NLP
As, Text Mining refers to
the process of deriving high quality
information from the text .
The overall goal is, essentially to turn
text into data for analysis, via
application of Natural Language
Processing (NLP)
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7. What is NLP ?
NLP: Natural Language Processing is a part of computer science
and artificial intelligence which deals with human languages.
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21. Stemming a Document
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1. Take a document as the input.
2. Read the document line by line
3. Tokenize the line
4. Stem the words
5. Output the stemmed words
Steps to stem a Document
22. Other Stemmmers
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• Snowball Stemmers
• ISRI Stemmer
• RSLPS Stemmer
1. Danish
2. Dutch
3. English
4. French
5. German
6. Hungarian
7. Italian
8. Norwegian
9. Porter
10. Portuguese
11. Romanian
12. Russian
13. Spanish
14. Swedish
23. Lemmatization
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• Groups together different inflected forms of a
word, called Lemma
• Somehow similar to Stemming, as it maps
several words into one common root
• Output of Lemmatisation is a proper word
• For example, a Lemmatiser should
map gone, going and went into go
24. Applications of
Stemming & Lemmatization
Sentimental
Analysis
Document
Clustering
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Information
Retrieval