Natural Language Processing

NLP Tasks and Applications, Language-Building Blocks, Challenges of NLP, Machine Learning for NLP – Naïve Bayes Classifier, Logistic Regression, Support Vector Machines, Approaches to NLP-Heuristics-Based NLP, Machine Learning-based NLP.

NLP System Pipeline Steps-Data Acquisition, Text Extraction and Clean-up, Pre- processing, Feature Engineering, Modelling, Evaluation, Post-Modelling Phases Text Representation-Vector Space Models, Basic Vectorization Approaches-One-Hot Encoding, Bag of Words, Bag of N-Grams, TF-IDF; Distributed Representations-Word Embeddings, Doc2Vec.

Text Classification-Text classification applications, Pipeline for building text classification systems, Naïve Bayes for Sentiment Classification – Naïve Bayes Classifier Training, Optimizing for Sentiment Analysis, Evaluation: Precision, Recall, F-measure; Logistic Regression- Classification: the sigmoid, Learning in Logistic Regression. Information Extraction (IE)-IE Applications, The General Pipeline for IE, Named Entity Recognition (NER) - Ambiguity in Named Entity Recognition, NER as Sequence Labeling, Evaluation of NER.

Relation Extraction-Using Patterns to Extract Relations, Relation Extraction via Supervised Learning, Semisupervised Relation Extraction via Bootstrapping, Distant Supervision for Relation Extraction, Evaluation of Relation Extraction system. Information Retrieval – Term weighting and document scoring, Inverted Index, Evaluation of Information Retrieval Systems.

Question-Answering Systems, Factoid Question Answering – Question Processing, Passage Retrieval, Answer Processing; Evaluation of Factoid Answers. Machine Translation – Why Machine Translation is Hard, Classical Machine Translation and the Vauquois Triangle– Direct Translation, Transfer; Statistical Machine Translation, The Phrase based Translation model, Alignment in MT, Training Alignment Models, Decoding for Phrase-based Statistical MT.