Advanced Natural Language Processing


Course organization

Instructor: Tatjana Scheffler
TA: Edit Szügyi
Time: Tuesdays & Fridays, 10 a.m.-12
Place: Golm, building 14, room 009
Modules: CSBM1 (M.Sc. Cognitive Systems)
Moodle: Please register on the course's Moodle site.


Grading policy

Passing the course

To be admitted to the module exam, you need to pass the course. For this, we will grade the best two assignments out of each half of the semester (i.e., the best 2 from the first 3 + the best 2 from the second 3). At least 250 points in total (out of 400) in these 4 assignments are needed to pass the course.

Module grade

The grade will be based on a collaborative final project, to be completed during the semester break. There are four graded deliverables for this project:

  1. a planning paper (individual)
  2. a project presentation (group)
  3. the implemented project (group)
  4. a project report (individual)

The grade will be composed equally from these four parts. Details will be discussed in class.

Course description

This class is the graduate-level introduction to computational linguistics, a first-year class in the MSc Cognitive Systems. The purpose of this class is to introduce the important concepts, models and methods used in natural language processing (NLP). After the successful completion of this course, students should be able to (i) read and understand the scientific literature in the area of computational linguistics and (ii) start implementing their own NLP projects.

We will cover the following topics:


Readings in J/M are marked for the third edition unless marked otherwise.

Date Topic Readings Assignments
T 10/16 Introduction
F 10/19 Review: Probability Theory Harald Goldstein, A short introduction to probability and related concepts
And/Or: Manning/Schütze, chapter 2.1
Optional: Kevin Murphy, Binomial and multinomial distributions
T 10/23 N-grams Jurafsky/Martin, chapters 3.1-3.2
A video about Zipf's law
A1 released
F 10/26 Smoothing language models Jurafsky/Martin, chapters 3.4-3.7
Opt.: Manning/Schütze, chapters 6.2-6.3
Chen/Goodman, 1998
T 30/10 Classification Jurafsky/Martin, chapter 4
F 11/2 Classification (2) Jurafsky/Martin, chapter 5 A1 due, A2 released
T 11/6 Part of speech tagging Jurafsky/Martin (2nd ed.!), chapters 5.1-5.5, 6.1-6.5; J/M (3rd ed.) ch. 8.1-8.4
F 11/9 discussion of A1 (Edit) be prepared to present your solutions!
T 11/13 HMM Training Jurafsky/Martin, rest of chapter 8 (-8.4) + Appendix A
HMM spreadsheet, Eisner's ice cream example
Eisner's paper explaining how to work with the spreadsheet
F 11/16 Context free grammars, CKY parsing Jurafsky/Martin, chapter 11 (-11.2),
J/M chapter 10 as background
further reading: Santorini/Kroch, Online syntax textbook
CKY animation
A2 due, A3 released
T 11/20 PCFGs Jurafsky/Martin, chapters 12.1-12.5, 12.7
F 11/23 discussion of A2 be prepared to present your solutions!
T 11/27 dies academicus (no class)
F 11/30 Training PCFGs Jurafsky/Martin, chapter 12
Manning/Schütze, chapter 11
Michael Collins, The inside-outside algorithm.
A3 due, A4 released
T 12/4 Advanced PCFG models Mark Johnson (1998), PCFG Models of Linguistic Tree Representations (esp. on parent annotations)
Michael Collins, Lexicalized PCFGs
Dan Klein/Chris Manning (2003), Accurate unlexicalized parsing
F 12/7 discussion of A3 be prepared to present your solutions!
T 12/11 Dependency parsing McDonald/Pereira/Ribarov/Hajic (2005), Non-projective Dependency Parsing using Spanning Tree Algorithms
Joakim Nivre (2008), Algorithms for Deterministic Incremental Dependency Parsing
F 12/14 Statistical machine translation: Alignments Jurafsky/Martin (2nd ed.), ch. 25 (through 25.6)
Adam Lopez, Word Alignment and the Expectation-Maximization Algorithm tutorial (try here)
MT Talks
A4 due
T 12/18 Phrase-based machine translation Jurafsky/Martin (2nd ed.), ch. 25
T 12/18, 12:30pm discussion of A3 be prepared to present your solutions!

no class (winter break)

no class (winter break)
T 1/8 Syntax-based machine translation David Chiang, Hierarchical phrase-based translation. Computational Linguistics, 2007. A5 distributed
F 1/11 Semantic parsing Zettlemoyer/Collins, Learning to Map Sentences to Logical Form. Structured Classification with Probabilistic Categorial Grammars. 2005
Wong/Mooney, Learning for Semantic Parsing with Statistical Machine Translation. HLT-NAACL, 2006
Opt.: Mark Steedman, A very short introduction to CCG. 1996
T 1/15 Lexical semantics Jurafsky/Martin, ch. 6 (+ Appendix C)
Further reading: Mitchell/Lapata (2008), Vector-based models of semantic composition; Baroni/Zamparelli (2010), Nouns are vectors, adjectives are matrices
F 1/18 Speech recognition + synthesis Jurafsky/Martin (2nd ed.), ch. 8,9
Links for further reading/system samples:
"Human parity" speech recognition
Language log on human parity speech recognition
Google speech API web demo
S. Germesin on using the speech API remotely
WaveNet: Generating raw audio
Deep Learning in Speech Synthesis
Expressive and emotional synthetic speech
A5 due, A6 released
T 1/22 LDA David Blei, Probabilistic topic models. 2012
Links for further reading:
Steyvers/Griffiths, Probabilistic Topic Models. 2007.
Darling, A Theoretical and Practical Implementation Tutorial on Topic Modeling and Gibbs Sampling.
Online Topic Modelling Tutorial and Demo for LDA
F 1/25 discussion of A5 be prepared to present your solutions!
T 1/29 Intro to deep neural networks Richard Socher, Recursive Deep Learning for Natural Language Processing and Computer Vision (Chapter 2). 2014
Further reading (for intuitions):,
F 2/1 presentations of final projects, final discussion all A6 due
2/4-10 no class


Most computational linguists own both of these books. We will assign weekly readings, so you should ensure you get your own copy or have access to the copies that are available in the university library.

Last modified: Tue Jan 22 10:02:16 CET 2019