EMPIRICAL METHODS FOR NATURAL LANGUAGE PROCESSING

CONTENT:

This course presents the fundamentals of the empirical approach for Natural Language Processing (NLP). The core of the contents consists of the following three topics:

Statistical methods for NLP
Machine Learning for PLN
The usage of ontologies

The different approaches will be presented from a generic point of view. The usefulness of the given methods and algorithms will be illustrated through the application to several common NLP tasks, including, among others: POS Tagging, Syntactic Analysis, and Word Sense Disambiguation. A special stress is given to the study of the automatic acquisition of lexical resources and language models from textual corpora.

Advisors

MARQUEZ VILLODRE, LUIS
PADRO CIRERA, LLUIS
RIGAU CLARAMUNT, GERMAN

Bibliography

[CM99] C. Cardie and R. Mooney. Guest Editor's Introduction: Machine Learning and Natural Language. Machine Learning. Special Issue on Natural Language Learning, 34(1/2/3):5-9, 1999.
[DMS00] R. Dale, H. Moisl and H. Somers. Handbook of Natural Language Processing. Marcel Dekker, New York, 2000.
[JM00] D. Jurafsky and J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, Upper Saddle River, N. J., 2000.
[Màr00] L. Màrquez. Machine Learning and Natural Language Processing. Technical Report LSI-00-45-R, Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, 2000.
[MS99] C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, 1999.
[WRS96] S. Wermter, E. Riloff and G. Scheler (eds.). Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Lecture Notes in Artificial Intelligence, 1040. Springer, 1996.
[YB97] s. Young and G. Bloothoft (eds.). Corpus-based Methods in Language and Speech Processing. An ELSNET book. Kluwer Academic Publishers, Dordrecht, 1997.