Computer Science 4750:
Introduction to Natural Language Processing
(Fall 2014)
Course Objectives / Description
Course objectives: To give students a broad introduction to the field of
natural language processing (NLP). The aim will be to give a working knowledge
of basic algorithms and data structures used to solve key NLP tasks (utterance
comprehension / utterance production / language acquisition) in both of
the major algorithmic paradigms used today (rule-based / statistical).
Course description: Tasks involving human languages, such as speech
recognition, text understanding, and keyword-based information retrieval
underlie many modern computing applications and their interfaces. To be truly
useful, such natural language processing must be both efficient and robust.
This course will give an introduction to the algorithms and data structures
used to solve key NP tasks, including utterance understanding and generation
and language acquisition, in both of the major algorithmic paradigms used today
(rule-based and statistical). The emphasis will be primarily on text-based
processing though speech-based processing will be addressed where possible.
Prerequisites
CS 2711 and (CS 2500 or equivalent experience programming with Python);
CS 3719 is recommended but not necessary
Evaluation Scheme (Tentative)
| Assignments (5) |
35% |
| Class Exams (2) |
40% |
| Student Presentations (2) |
5% |
| Course Project |
20% |
Course Outline (Tentative)
- Background: Linguistics and Language Processing (3 weeks)
(Overview of classical linguistics; representations of
natural language utterances, grammars, and lexicons;
implementations of processes on natural language
representations)
- Utterance comprehension (2 1/2 weeks)
- Utterance production (1 week)
- Language acquisition (1 week)
- Special applications (1 1/2 weeks)
(e.g., language-language translation, question answering, text mining)
- Student presentations (2 weeks)
Textbook
- Bird, S., Klein, E., and Loper, E. (2009) Natural Language Processing with
Python. O'Reilly Media.
References / Reading List (Selected)
- Indurkhya, N. and Damerau, F.J. (eds.) (2010) Handbook of Natural
Language Processing (2nd Edition). Chapman and Hall / CRC.
- Jurafsky, D. and Martin, J.H. (2008) Speech and Natural Language
Processing (2nd Edition). Prentice-Hall.
- Manning, C. and Scheutze, H. (1999) Foundations of Statistical Natural
Language Processing. The MIT Press.
- Mitkov, R. (ed.) (2005) The Oxford Handbook of Computational
Linguistics. Oxford University Press.
- Roche, E. and Schabes, Y. (eds.) (1997) Finite-state Natural Language
Processing. The MIT Press.
Created: April 14, 2014
Last Modified: April 14, 2014