Introduction to Natural Language Processing

Spring Semester 2015

This course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.

The objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.

Course Catalogue Info


13/02/2015 Thee webpage for this year's course is online.
02/03/2015 Please see the project page for any details about the project.

Course Overview

1 16/02/2015 Introduction & Project overview (EA,MC) Slide set 1Slides project
2 23/02/2015 Entity Disambiguation (MC) Slide set 2 Exercise 2
3 02/03/2015 Finite State Automata and Morphology (EA) Slide set 3 Chapters 2,3
4 09/03/2015 POS tagging and HMMs (MC) Slide set 4 Chapter 5,6 Exercise 4
Additional Material
5 16/03/2015 Language Models (EA) Slide set 5 Chapter 4
6 23/03/2015 Machine Translation (MC) Slide set 6
7 30/03/2015 Lexical Semantics (EA) Slide set 7 Exercise 7
8 20/04/2015 Text Summarization (EA) Slide set 8 Exercise 8
9 27/04/2015 Event Understanding (EA) Slide set 9 Exercise 9
10 04/05/2015 Grammars and Parsing (MC) Slide set 10
11 11/05/2015 Statistical and Dependency Parsing (MC) Slide set 11
12 18/05/2015 Stundent Projects (EA,MC)

Graded Projects

Entity Linking for Search Queries

due: Mai 18 Details


Monday 13 - 15 h CAB G 51

Exercise groups (starting february 23)

1 Monday 15 - 16 h CAB G 51 Florian Schmidt


Lecturer Massimiliano Ciaramita
Lecturer Enrique Alfonseca
Assistant Florian Schmidt


Speech and Language Processing - An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (Second Edition), by Daniel Jurafsky and James H. Martin