COMP5046 NLP Complete Course Notes (Distinction)

Description

These COMP5046 notes include my raw personal lecture notes, homework answers, laboratory examples, and textbook summaries. I received a final mark of 80 (Distinction). Contents ---------- Course information +-- Assignments +-- Lab exercises Lecture 1 +-- Introduction to NLP +-- Word representation +-- +-- WordNet +-- +-- One-hot vectors Lecture 02 +-- More word representation +-- +-- Bag of words (BoW) +-- +-- Term frequency-inverse document frequency (TFIDF) +-- Prediction-based word representation +-- +-- Word2Vec +-- +-- FastText +-- +-- GloVe Lecture 03 +-- Word embedding evaluation +-- Deeplearning in NLP Lecture 04 +-- Machine learning and NLP +-- Seq2Seq learning +-- +-- Recurrent NN (RNN) +-- +-- Long short-term memory (LSTM) +-- +-- Gated Recurrent Unit (GRU) +-- Seq2Seq encoding and decoding +-- RNNs +-- Other Lecture 05 +-- Chatbots +-- +-- Goal-oriented conversational agents +-- +-- Initiative +-- +-- Chat-oriented conversational agents +-- Language Fundamentals +-- +-- Levels of NLP +-- +-- Text preprocessing Lecture 06 +-- Part of Speech (POS) Tagging +-- Baseline approaches +-- Probabilistic approaches +-- Deep-learning approaches Lecture 07 +-- Dependency structure +-- Dependency parsing algorithms +-- Evaluation Lecture 08 +-- Language model +-- +-- Traditional language model +-- +-- Neural language model +-- Natural language generation (NLG) +-- +-- Other approaches +-- Evaluation Lecture 09 +-- Information extraction +-- Named entity recognition (NER) +-- +-- Evaluation +-- +-- Rule-based systems +-- +-- Statistical approaches +-- +-- Seq2seq for NER +-- Co-reference resolution +-- +-- Mention pair +-- +-- Mention ranking Lecture 10 +-- Relation extraction +-- +-- Hand-built (pattern or rule-based) approach +-- +-- Supervised approach +-- +-- Semi-supervised / Distant approach +-- Sentiment analysis Lecture 11 +-- Question-Answering +-- +-- Knowledge-based +-- +-- IR-based +-- +-- VIsual question-answering Lecture 12 +-- Machine translation (MT) +-- +-- Rule-based MT +-- +-- Statistical MT +-- +-- Neural MT +-- In general +-- +-- How to evaluate machine translation? +-- +-- Overall issues with machine translation Lecture 13 +-- Review +-- Exam +-- +-- Samples questions Miscellaneous +-- Week 06 +-- Week 07 +-- Week 08 +-- Week 09 +-- Week 13