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

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

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

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

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

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.

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

Aim: Implement Naive Bayes classifier