Tokenization – Rock The IT

Natural Language Processing Practical

Dr.Mahendra Kanojia — Thu, 10 Mar 2022 18:28:06 +0000

Course Information

Categories: Bachelor of Engineering : CS and IT, BCA, BE, Beginner, BSc Computer Science, BSc IT, MCA, ME, Msc Computer Science, MSc IT, MSc IT Part 1 (New Syllabus CBGS), MSc IT Part 1 (OLD Syllabus Yearly Pattern), MSc IT Part 2 (New Syllabus CBGS), MSc IT Part 2 (OLD Syllabus Yearly Pattern)

Tags: Compare two nouns, Convert audio file Speech to Text, Convert the given text to speech, Corpus, Genism, Handling stopword., Identify the Indian language of a text, Implement Naive Bayes classifier, Indian Languages, Install NLTK, Keras, LancasterStemmer, lemmas, Map Words to Properties Using Python Dictionaries, Natural Language Processing, NLP, NLP Practical, Part of speech Tagging and chunking of user defined text., Practical, RegexpStemmer, SnowballStemmer Study WordNetLemmatizer, spaCy, Speech Tagging, Study Conditional frequency distributions Study of tagged corpora with methods like tagged_sents, Text Tokenization, Tokenization, Tokenization using Keras, Tokenization using NLTK, Tokenization using Python’s split() function, Tokenization using Regular Expressions (RegEx), Tokenization using the spaCy library, Wordnet, Write a program to find the most frequent noun tags, y PorterStemmer

Course Instructor

Dr.Mahendra Kanojia Author

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Aim: a. Install NLTK b. Convert the given text to speech c. Convert audio file Speech to Text.

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Aim: Study of various Corpus – Brown, Inaugural, Reuters, udhr with various methods like fields, raw, words, sents, categories.

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Aim: a. Study of Wordnet Dictionary with methods as synsets , definitions, examples, antonyms. b. Study lemmas, hyponyms, hypernyms, entailments,

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Aim: Text Tokenization a. Tokenization using Python’s split() function b. Tokenization using Regular Expressions (RegEx) c. Tokenization using NLTK d. Tokenization using the spaCy library e. Tokenization using Keras f. Tokenization using Gensim

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Aim: Important NLP Libraries for Indian Languages and perform: a. word tokenization in Hindi b. Generate similar sentences from a given Hindi text input c. Identify the Indian language of a text

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Aim: Illustrate part of speech tagging. a. Part of speech Tagging and chunking of user defined text. b. Named Entity recognition of user defined text. c. Named Entity recognition with diagram using NLTK corpus – treebank

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Aim: a. Define grammer using nltk. Analyze a sentence using the same. b. Accept the input string with Regular expression of FA: 101+ c. Accept the input string with Regular expression of FA: (a+b)*bba d. Implementation of Deductive Chart Parsing using context free grammar and a given sentence.

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Aim: Study PorterStemmer, LancasterStemmer, RegexpStemmer, SnowballStemmer Study WordNetLemmatizer

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Aim: Implement Naive Bayes classifier

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