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NLP PPT.pptx

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Lipika Sharma Shrivastava
Lipika Sharma Shrivastava

The document provides an overview of natural language processing (NLP) including definitions, applications, modeling techniques, and tools used. It defines NLP as making computers understand human language and discusses applications like email filters, assistants, translation, and data analysis. Techniques covered include data preprocessing, tokenization, stop words removal, stemming, lemmatization, bag of words, TF-IDF, word embeddings, and sentiment analysis. Python is highlighted as a commonly used programming language and libraries like NLTK are mentioned. Demos are provided of tokenization, stemming, lemmatization, and sentiment analysis.

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Presented By: Lipika Sharma
Interaction Hello How are you? I am great; thanks for asking. How was your day?
Chatbots Do you remember the chatbot you interacted with in last ? https://www.pandorabots.com/mitsuku/ chatterbot-corpus/chatterbot_corpus/data/english at master · gunthercox/chatterbot-corpus · GitHub
What is NLP? Natural language processing (NLP) is an integral part of AI, Computer Science, and Linguistics. NLP is all about making computers/machines as intelligent as human beings in the understanding of natural-communication language like text, speech, and so on. It comprises 2 major functionalities. they are Human to machine translation and Machine to Human translation.
Applications of NLP •Email filters. Email filters are one of the most basic and initial applications of NLP online. ... •Smart assistants. ... •Search results. ... •Predictive text. ... •Language translation. ... •Digital phone calls. ... •Data analysis. ... •Text analytics.
Modelling Techniques Data Preprocessing Tokenization Stop Words Removal Stemming Lemmatization Bag of Words TF-IDF Word Embeddings Sentiment Analysis Steps towards NLP
Tool Used - Python Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
Python Library • NumPy • Pandas • Matplotlib • Seaborn • NLTK
Art to read the data Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format.. Demo -
Tokenization – Tokenization is a process by which sensitive data elements such as PANs, Personally Identifiable Information elements, etc. are replaced by surrogate values, or tokens. Tokenization (or “masking”, or “obfuscation”) means some form of format- preserving data protection: converting sensitive values into non- sensitive, replacement values – tokens – the same length and format of the original data.
•Tokens share some characteristics with the original data elements, such as format, length, etc •Each data element is mapped to a unique token. •Tokens are deterministic: repeatedly generating a token for a given value yields the same token. •A tokenized database can be searched by tokenizing the query terms and searching for those.
Demo
Stemming – Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma.
Advantage of Stemming • Stemming is a useful "normalization" technique for words • Stemming is used in information retrieval systems like search engines. • It is used to determine domain vocabularies in domain analysis. • Stemming is faster because it chops words
Fun Fact - • Google search adopted a word stemming in 2003. Previously a search for “fish” would not have returned “fishing” or “fishes”.
Demo
Lemmatization – Lemmatization is a text normalization technique used in Natural Language Processing (NLP). Essentially, lemmatization is a technique that switches any kind of a word to its base root mode. (Lemma)
Difference Stemming is a process that stems or removes last few characters from a word, often leading to incorrect meanings and spelling. Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma.
Stemming vs Lemmatization Stemming • Stemming is a process that stems or removes last few characters from a word, often leading to incorrect meanings and spelling. • For instance, stemming the word ‘Caring‘ would return ‘Car‘. • Stemming is used in case of large dataset where performance is an issue. • It is faster to process Lemmatization • Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma. • For instance, lemmatizing the word ‘Caring‘ would return ‘Care‘. • Lemmatization is computationally expensive since it involves look-up tables and what not. • It is slower
Demo
Stop Words– Stop words are a set of commonly used words in a language. Examples of stop words in English are “a”, “the”, “is”, “are” and etc. Stop words are commonly used in Text Mining and Natural Language Processing (NLP) to eliminate words that are so commonly used that they carry very little useful information.
Sample Text with Stop Words Sample Text without Stop Words Aarush Coaching Classes – A stem learning place for kids Aarush Coaching Classes, Stem, Learning, Place, kids Can Listening be exhausting ? Listening, Exhausting I like Teaching, so I teach Like, Teaching, Teach Stop Words Example
Demo
Modelling Techniques in NLP Bag of Words TF-IDF Word Embeddings Sentiment Analysis
Bag of Words A bag-of-words is a representation of text that describes the occurrence of words within a document. It involves two things: A vocabulary of known words. A measure of the presence of known words.
The Bag-of-words model is an orderless document representation — only the counts of words matter. For instance, in the above example "John likes to watch movies. Mary likes movies too", the bag-of-words representation will not reveal that the verb "likes" always follows a person's name in this text. Bag of Words - Example
TF-IDF TF -IDF short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
TF –IDF Explanation • TF – IDF is multiplication of two values TF and IDF • TF is the frequency of term divided by a total number of terms in the document • IDF is obtained by dividing the total number of documents by the number of documents containing the term and then taking the logarithmic of that quotient.
Formula
Steps
NLP PPT.pptx
NLP PPT.pptx
NLP PPT.pptx
That's it 😃! the text is now ready to feed into a machine learning algorithm.
Word Embeddings A word embedding is a learned representation for text where words that have the same meaning have a similar representation.
Types Word Embeddings Types Word2vec Glove fastText
Sentiment Analysis Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text.. “I really like the new design of your website!” → Positive “The new design is awful!” → Negative
NLP PPT.pptx
Machine Learning Algorithm
https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews Reference :
Sentiment Analysis - Demo
Components of NLP
Difference
Phases of NLP
• Less costly than employing human staff • Provides quicker customer service response times • Easy to implement) Advantages of NLP
Adieu in NLP Style https://github.com/lipika-tech Connect with me : https://www.youtube.com/c/aarushcoachingclasses

More Related Content

NLP PPT.pptx

  • 2. Interaction Hello How are you? I am great; thanks for asking. How was your day?
  • 3. Chatbots Do you remember the chatbot you interacted with in last ? https://www.pandorabots.com/mitsuku/ chatterbot-corpus/chatterbot_corpus/data/english at master · gunthercox/chatterbot-corpus · GitHub
  • 4. What is NLP? Natural language processing (NLP) is an integral part of AI, Computer Science, and Linguistics. NLP is all about making computers/machines as intelligent as human beings in the understanding of natural-communication language like text, speech, and so on. It comprises 2 major functionalities. they are Human to machine translation and Machine to Human translation.
  • 5. Applications of NLP •Email filters. Email filters are one of the most basic and initial applications of NLP online. ... •Smart assistants. ... •Search results. ... •Predictive text. ... •Language translation. ... •Digital phone calls. ... •Data analysis. ... •Text analytics.
  • 6. Modelling Techniques Data Preprocessing Tokenization Stop Words Removal Stemming Lemmatization Bag of Words TF-IDF Word Embeddings Sentiment Analysis Steps towards NLP
  • 7. Tool Used - Python Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
  • 8. Python Library • NumPy • Pandas • Matplotlib • Seaborn • NLTK
  • 9. Art to read the data Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format.. Demo -
  • 10. Tokenization – Tokenization is a process by which sensitive data elements such as PANs, Personally Identifiable Information elements, etc. are replaced by surrogate values, or tokens. Tokenization (or “masking”, or “obfuscation”) means some form of format- preserving data protection: converting sensitive values into non- sensitive, replacement values – tokens – the same length and format of the original data.
  • 11. •Tokens share some characteristics with the original data elements, such as format, length, etc •Each data element is mapped to a unique token. •Tokens are deterministic: repeatedly generating a token for a given value yields the same token. •A tokenized database can be searched by tokenizing the query terms and searching for those.
  • 12. Demo
  • 13. Stemming – Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma.
  • 14. Advantage of Stemming • Stemming is a useful "normalization" technique for words • Stemming is used in information retrieval systems like search engines. • It is used to determine domain vocabularies in domain analysis. • Stemming is faster because it chops words
  • 15. Fun Fact - • Google search adopted a word stemming in 2003. Previously a search for “fish” would not have returned “fishing” or “fishes”.
  • 16. Demo
  • 17. Lemmatization – Lemmatization is a text normalization technique used in Natural Language Processing (NLP). Essentially, lemmatization is a technique that switches any kind of a word to its base root mode. (Lemma)
  • 18. Difference Stemming is a process that stems or removes last few characters from a word, often leading to incorrect meanings and spelling. Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma.
  • 19. Stemming vs Lemmatization Stemming • Stemming is a process that stems or removes last few characters from a word, often leading to incorrect meanings and spelling. • For instance, stemming the word ‘Caring‘ would return ‘Car‘. • Stemming is used in case of large dataset where performance is an issue. • It is faster to process Lemmatization • Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma. • For instance, lemmatizing the word ‘Caring‘ would return ‘Care‘. • Lemmatization is computationally expensive since it involves look-up tables and what not. • It is slower
  • 20. Demo
  • 21. Stop Words– Stop words are a set of commonly used words in a language. Examples of stop words in English are “a”, “the”, “is”, “are” and etc. Stop words are commonly used in Text Mining and Natural Language Processing (NLP) to eliminate words that are so commonly used that they carry very little useful information.
  • 22. Sample Text with Stop Words Sample Text without Stop Words Aarush Coaching Classes – A stem learning place for kids Aarush Coaching Classes, Stem, Learning, Place, kids Can Listening be exhausting ? Listening, Exhausting I like Teaching, so I teach Like, Teaching, Teach Stop Words Example
  • 23. Demo
  • 24. Modelling Techniques in NLP Bag of Words TF-IDF Word Embeddings Sentiment Analysis
  • 25. Bag of Words A bag-of-words is a representation of text that describes the occurrence of words within a document. It involves two things: A vocabulary of known words. A measure of the presence of known words.
  • 26. The Bag-of-words model is an orderless document representation — only the counts of words matter. For instance, in the above example "John likes to watch movies. Mary likes movies too", the bag-of-words representation will not reveal that the verb "likes" always follows a person's name in this text. Bag of Words - Example
  • 27. TF-IDF TF -IDF short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
  • 28. TF –IDF Explanation • TF – IDF is multiplication of two values TF and IDF • TF is the frequency of term divided by a total number of terms in the document • IDF is obtained by dividing the total number of documents by the number of documents containing the term and then taking the logarithmic of that quotient.
  • 30. Steps
  • 34. That's it 😃! the text is now ready to feed into a machine learning algorithm.
  • 35. Word Embeddings A word embedding is a learned representation for text where words that have the same meaning have a similar representation.
  • 37. Sentiment Analysis Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text.. “I really like the new design of your website!” → Positive “The new design is awful!” → Negative
  • 45. • Less costly than employing human staff • Provides quicker customer service response times • Easy to implement) Advantages of NLP
  • 46. Adieu in NLP Style https://github.com/lipika-tech Connect with me : https://www.youtube.com/c/aarushcoachingclasses