Advanced Natural Language Processing


Course organization

Instructor: Tatjana Scheffler
TA: Jens Johannsmeier
Time: Tuesdays, 12-2 p.m.
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:

The grade will be based on a final project, to be completed during the semester break. The grade takes the following criteria into account: proposal and its presentation, difficulty/creativity, correctness/performance, code readability/documentation.

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:


Date Topic Readings Assignments
T 10/18 Introduction
F 10/21 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
A1 released
T 10/25 practical concerns (TA) notes for assignment
F 10/28 N-grams Jurafsky/Martin, chapters 4.1-4.2
A video about Zipf's law
T 11/1 Smoothing Jurafsky/Martin, chapters 4.5-4.7
Opt.: Manning/Schütze, chapters 6.2-6.3
F 11/4 Part of Speech Tagging Jurafsky/Martin, chapters 5.1-5.5, 6.1-6.4 A1 due, A2 released
T 11/8 HMM Training Jurafsky/Martin, chapters 5.7-5.8, 6.5
HMM spreadsheet, Eisner's ice cream example
F 11/11 discussion of A1 be prepared to present your solutions!
T 11/15 Context-Free Grammars & Parsing (CKY) Jurafsky/Martin, chapter 13 (-13.4),
J/M chapter 12 as background
F 11/18 Probabilistic Context-Free Grammars Jurafsky/Martin, chapters 14.1-14.5, 14.7 A2 due, A3 released
T 11/22 Training PCFGs Jurafsky/Martin, chapter 14.3
Manning/Schütze, chapter 11
Michael Collins, The inside-outside algorithm.
F 11/25 discussion of A2 be prepared to present your solutions!
T 11/29 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 12/2 Dependency parsing McDonald/Pereira/Ribarov/Hajic (2005), Non-projective Dependency Parsing using Spanning Tree Algorithms
Joakim Nivre (2008), Algorithms for Deterministic Incremental Dependency Parsing
A3 due, A4 released
T 12/6 Speech recognition Jurafsky/Martin, ch. 9
Links for further reading:
"Human parity" speech recognition
Language log on human parity speech recognition
Google speech API web demo
S. Germesin on using the speech API remotely
F 12/9 discussion of A3 be prepared to present your solutions!
Joshua Goodman, Semiring Parsing. Computational Linguistics, 1999.
T 12/13 Speech synthesis Jurafsky/Martin, ch. 8
F 12/16 no class A4 due

no class (winter break)

no class (winter break)
T 1/3 Statistical machine translation: alignments Jurafsky/Martin, ch. 25 (through 25.6)
Adam Lopez, Word Alignment and the Expectation-Maximization Algorithm tutorial (try here)
MT Talks
final project guidelines
F 1/6 discussion of A4 be prepared to present your solutions! A5 released
T 1/10 Phrase-based machine translation Jurafsky/Martin, ch. 25
F 1/13 Syntax-based machine translation David Chiang, Hierarchical phrase-based translation. Computational Linguistics, 2007. project proposal due!
T 1/17 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
F 1/20 Lexical semantics Jurafsky/Martin, chs. 19 + 20
Further reading: Mitchell/Lapata (2008), Vector-based models of semantic composition; Baroni/Zamparelli (2010), Nouns are vectors, adjectives are matrices
A5 due, A6 released
T 1/24 LDA David Blei, Probabilistic topic models. 2012
F 1/27 discussion of A5 be prepared to present your solutions!
T 1/31 NLP for discourse Webber/Egg/Kordoni, Discourse Structure and Language Technology. 2012. (accessible from the U Potsdam network)
We'll discuss the following papers on Bayesian pragmatics for discourse relations:
Yung et al., 2016a, Yung et al., 2016b
F 2/3 no class (instructor away) A6 due
T 2/7 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 2/10 discussion of A6 + presentations of final projects 10-minute presentations of ideas, approaches, preliminary results


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 Apr 4 09:34:36 CEST 2017