cm3060 Natural Language Processing

Main Info

• Title: Natural Language Processing

• Leader(s): Tony Russell-Rose

• Semester Taken: October 2021

Abstract

An Introduction to natural language processing.

Syllabus

Available on the REPL

Main Textbooks

• Jurafsky and Martin: Speech and Language Processing

• Bird et al: The NLTK Book

Supplementary Lectures

Jurafsky and Manning’s NLP Lectures

Bitesize lectures that go into more depth on most of the course (and more) than the UoL ones.

Supplementary Texts

NLP Introductions:

• Manning and Schutze: Foundations of Statistical NLP

• Eisenstein: Introduction to Natural Language Processing

Deep Learning Approaches:

Deep learning approaches are entirely missing from the course. A good intro is Chollet: Chapter 11: Deep Learning for Text

See also:

• Tunstall et al: Natural Language Processing with Transformers

Linguistics:

• Yule: The Study of Language

• Burton-Roberts: Analysing Sentences

• Cann: Formal Semantics

Information Retrieval:

• Manning et al: Introduction to Information Retrieval

Topics

• Topic 1: Introduction

• Topic 2: Basic Text Processing

• Topic 3: Language modelling: statistical LMs, traditional and distributional approaches: Language Modelling

• Topic 4: Lexical semantics: curated approaches, word embeddings: Lexical Semantics

• Topic 5: Text categorization: topical analysis, sentiment analysis: Categorization

• Topic 6: Syntax: POS tagging, syntactic parsing: Syntax and Parsing

• Topic 7: Information extraction: relation extraction, NLP pipelines: Information Extraction

• Topic 8: Information retrieval: TF.IDF, query processing: Information Retrieval

• Topic 9: Dialog systems: speech I/O, chatbots: Chatbots

• Topic 10: Case studies: interviews with practicing NLP scientists, covering evaluation & ethical issues: NLP in Practice