Lectures

“Introduction to the course”

• Chapter 4: Naive Bayes classification and sentiment in Jurafsky and Martin (3rd edition).

“In this lecture, we will discuss language models, i.e. modelling word sequences, and part-of-speech tagging”

• Part-of-speech tagging video can be found here

• Chapter 3: Language modelling with n-grams in Jurafsky and Martin (3rd edition).

• Chapter 8: Part-of-speech tagging in Jurafsky and Martin (3rd edition).

“In this lecture, we will discuss morphological processing and syntactic parsing”

• Lecture notes on moprphology are available here

• Chapter 12: Constituency grammars in Jurafsky and Martin (3rd edition).

• Chapter 13: Constituency parsing in Jurafsky and Martin (3rd edition).

“In this lecture, we will introduce statistical models of word meaning”

• Chapter 19: Word Senses and WordNet in Jurafsky and Martin (3rd edition).

• Chapter 6: Vector semantics and embeddings in Jurafsky and Martin (3rd edition).

“In this lecture, we will discuss generalisation from words to semantic classes and learning dense vector representations - word embeddings.”

• Chapter 6: Vector semantics and embeddings in Jurafsky and Martin (3rd edition).

• A gentle introduction to neural networks can be found here

• The following paper provides a nice explanation of skip-gram with negative sampling: Yoav Goldberg and Omer Levy. word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method

“In this lecture, we will discuss compositional semantics, i.e. modelling the meaning of phrases and sentences, and learning neural representations of sentences.”

• Chapter 7: Neural networks and neural language models in Jurafsky and Martin (3rd edition).

• Chapter 9: Sequence processing with recurrent neural networks in Jurafsky and Martin (3rd edition).

• A good and general reference for Neural Networks in NLP: Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing

• A gentle introduction to LSTMs is available here

• This is one of the papers that have introduced tree LSTM models: Kai Sheng Tai, Richard Socher, and Christopher D. Manning. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

“In this lecture, we will discuss discourse processing, i.e. modelling larger text fragments.”

• Chapter 22: Coreference resolution in Jurafsky and Martin (3rd edition).

• Chapter 23: Discourse coherence in Jurafsky and Martin (3rd edition).

“In this session, we will discuss applications of deep learning to sequence processing tasks, which have seen earlier in the course.”

“In this lecture, we will introduce an important NLP application – dialogue modelling.”

• Chapter 26: Dialogue systems and chatbots in Jurafsky and Martin (3rd edition).

“In this lecture, we will talk about language generation and cover a particular language generation task, text summarisation, in more detail.”

• A survey of recent summarisation techniques is available here

• An introduction to sequence-to-sequence models: Sequence to Sequence Learning with Neural Networks

“We will discuss the fundamentals of machine translation, including word-based / alignment models and phrase-based SMT”

• Neural Encoder-Decoder

• The Annotated Encoder-Decoder blog post

“We will discuss different kinds of social biases that NLP models may inadvertently encode.”

“In this lecture, we will give you a brief summary of the course and how different areas we have studied interface. We will also discuss what to expect at the exam.”