Course Plan

List of Experiments

S.No	Description of the Experiment
1	To implement text preprocessing techniques such as tokenization, case folding, stemming, lemmatization, stop word removal and calculate the edit distance between text strings.
2	To construct a Part-of-Speech (POS) tagger using the Hidden Markov Model (HMM) and implement the Viterbi algorithm to decode the most probable sequence of tags for a given sentence.
3	To implement word embedding using TF-IDF, Bag of Words, Word2Vec and Glove techniques for a given sentence.
4	To implementing n-gram language model with Lapalace smooting
5	Introduction to Named Entity Recognition (NER) and Its Implementation Using NLTK.
6	To implement review classification on IMDB dataset using RNN.
7	To implementing review classification on IMDB dataset using LSTM.
8	To construct Context-Free Grammars (CFGs)to generate sentences applying top-down and bottom-up parsing algorithms for syntactic analysis.
9	To implement Probabilistic Context-Free Grammars (PCFGs) for statistical parsing and explore dependency parsing using graph-based and transition-based methods for syntactic analysis.
10	To build a Neural Machine Translation model using sequence-to-sequence (Seq2Seq) architecture with attention mechanisms.

Class Notes & PPTs

1. - PPT