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Natural Language Processing

Code 11535
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements Solid programming skills.
Mode of delivery Face to face.
Work placements Not aplicable.
Learning outcomes Study of the main fundamental subjects from Human Language Technology, with a special focus on written language. Analysis of a number of theoretical principles and and practical techniques involved in human language processing.
At the end of the course the student should know the main themes of the Human Language Technology, especially in terms of written language. This includes the acquisition of theoretical knowledge of Natural Language Processing and its fundamental principles, at a morphological, syntactical, semantic, and pragmatic levels. The student should also gain mastery with several tools and technologies within those areas, which were being experienced through a number of exercises and assignments, implemented in laboratory.
Syllabus Part I - Foundations
1. Classical Introduction
2. Empirical Introduction

Part II - Theories
3. Modeling Language
4. Polilexical Units
5. Segmentation into Topics
6. Lexical Chains

Part III - Applications
7. Information Retrieval
8. Information Extraction
9. Text Classification and Clustering
10.Automatic Summarization
Main Bibliography Main Bibliography
====================
Jurafsky, D., Martin, J. (2008). “SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition”, Second Edition. McGraw Hill. ISBN: 978-0131873216.

Secondary Bibliography
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Manning, D., Schütze, H. (1999). “Foundations of Statistical Natural Language Processing”. The MIT Press. ISBN: 978-0262133609.

Bird, S., Klein, E., Loper, E. (2009). “Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit”. O'Reilly Media. ISBN: 978-0596516499.

Reese, R. (2015). “Natural Language Processing with Java (Community Experience Distilled)”. Packt Publishing. ISBN: 978-1784391799.
Teaching Methodologies and Assessment Criteria The evaluation will comprise a written component, in the form of two written tests (F1 and F2) and a practical work (TP) of application and consolidation of the acquired knowledge. Thus, the classification by "frequência" (CF) is calculated by weighing the classifications obtained in these three components:

CF = 30% F1 + 30% F2 + 40% TP.

In the case of CF < 6 values the student will not be admitted to the exam, failing the course. If CF = 9.5 the student does not need to take the exam, in order to be approved. If he/she goes to the exam, the best classification will always be maintained. The classification under examination (EC) is calculated as follows:

EC = 60% Exam + 40% TP
Language Portuguese. Tutorial support is available in English.
Last updated on: 2019-07-09

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