Probabilistic Models in NLP

This is the second course of the Natural Language Processing Specialization.

Week 1: Auto-correct using Minimum Edit Distance

• Create a simple auto-correct algorithm using minimum edit distance and dynamic programming

Week 2: Part-of-Speech (POS) Tagging

• Apply the Viterbi algorithm for POS tagging, which is important for computational linguistics

Week 3: N-gram Language Models

• Write a better auto-complete algorithm using an N-gram model (similar models are used for translation, determining the author of a text, and speech recognition)

Week 4: Word2Vec and Stochastic Gradient Descent

• Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model