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Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka

( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** ) <br>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: <br><br>Introduction to Big Data <br>What is Text Mining? <br>What is NLP? <br>Introduction to Stemming <br>Introduction to Lemmatization <br>Applications of Stemming & Lemmatization <br>Difference between stemming & Lemmatization <br><br>Follow us to never miss an update in the future. <br><br>Instagram: https://www.instagram.com/edureka_learning/ <br>Facebook: https://www.facebook.com/edurekaIN/ <br>Twitter: https://twitter.com/edurekain <br>LinkedIn: https://www.linkedin.com/company/edureka

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Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka

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  1. Agenda edureka! 1. What is Natural Language Processing? Anonymous 2. NLP Components Made by Code 3. Stemming 4. Lemmatization 5. Applications of Stemming & Lemmatization 6. The differences between the Two Powered by Citizens of the Internet Trust!

  2. edureka! The Human Language 6500

  3. edureka! Percentage The 21stCentury Unstructured Structured

  4. edureka! What is Text Mining ? Text Mining / Text Analytics is the process of deriving meaningful information from natural language text

  5. edureka! 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)

  6. edureka! What is NLP ? NLP: Natural Language Processing is a part of computer science and artificial intelligence which deals with human languages.

  7. Anonymous Stemming Tokenization Lemmatization POS Tags Named Entity Recognition Chunking

  8. edureka!

  9. edureka! Stemming Stemming Lemmatization Lemmatization 1960’s

  10. edureka! Stemming Stemming Lemmatization Lemmatization 1960’s

  11. edureka! miss

  12. edureka! misses

  13. edureka! missing

  14. edureka! NLTK

  15. edureka! NLTK NLTK

  16. edureka! Stemming Stemmingis the process of reducing inflection in words to their “root” forms such as mapping a group of words to the same Stem

  17. edureka! Stemming Stemmingis the process of reducing inflection in words to their “root” forms such as mapping a group of words to the same Stem Porter 1979 Lancaster 1990

  18. edureka! Stemming Porter1979 • Suffix Stripping • 5 Rules • Step By Step

  19. edureka! Stemming Lancaster1990 • Paice-Husk stemmer • Iterative Algorithm • Over Stemming may occur

  20. edureka! Stemming a Document Steps to stem a Document 1. 2. Read the document line by line 3. Tokenize the line 4. Stem the words 5. Output the stemmed words Take a document as the input.

  21. edureka! Other Stemmmers 1. 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 Danish • Snowball Stemmers Snowball Stemmers • ISRI Stemmer ISRI Stemmer • RSLPS Stemmer RSLPS Stemmer

  22. edureka! Lemmatization • 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

  23. edureka! Applications of Stemming & Lemmatization Sentimental Analysis Document Clustering Information Retrieval

  24. edureka! Stemming Lemmatization Actual Language Word Might not be an Actual Language Word Predefine Steps Uses WordNet Corpus

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