2016/2017

2017/2018

DM270

L-LIN/01 (GLOTTOLOGY AND LINGUISTICS)

DEPARTMENT OF HUMANITIES

THEORETICAL AND APPLIED LINGUISTICS; LINGUISTICS AND MODERN LANGUAGES

PERCORSO COMUNE

2°

2nd semester (26/02/2018 - 01/06/2018)

6

36 lesson hours

Italian

ORAL TEST

PRODANOF IRINA RALUCA (titolare) - 6 ECTS

Knowledge of fundamentals of computational linguistics is recommended

We are living in an information society in which computers, mobiles and Internet have a central role. One of the focused problems is a rapid access to information and information extraction, questioning the web. There is an increasing request for applications that need robust natural language processing: improve web-search engines, provide services delivered by man-machine interaction systems, machine translation, E-learning, information extraction and opinion mining, open-domain question answering systems seeking information from a huge amount of documents, and much more.
These applications need techniques for automatic treatment of natural language, information extraction and web mining on one hand and, on the other hand, large collections of annotated corpora.
The course aims at giving the fundamentals of natural language processing, an overview of computational models, algorithms and tools for different levels of analysis of language, going from morphology up to pragmatics

The course consists of two parts:
I. Theoretical and formal aspects of Computational Linguistics
1. formal grammars and automata. Parsing.
2. Finite-state techniques for morphological analysis. Tagger.
3. Semantic lexica. First order logics and lambda calculus.

II. Discourse analysis.
Discourse analysis is one of the most challenging tasks of natural language processing. Many applications as information extraction, Open Domain Question Answering, opinion and emotion mining, narratives, etc. need discourse analysis. Different theoretical approaches and computational models concerning aspects of discourse analysis (discourse structure, coherence and cohesion, anaphora resolution, information structure, etc.) will be presented as well as subjectivity mining and event and temporal relations detection.

Power-Point presentation
Lectures on Machine Learning techniques for NLP and distributional semantics

D. Jurafsky & James Martin, Speech and Language Processing, Prentice Hall, 2000;

James Allen, Natural Language Understanding (2nd ed.), Benjamin/Cummings, 1995

Florian Wolf, Edward Gibson, 2005. Representing Discourse Coherence. A Corpus-Based Study, in ACL
Mann, William C. and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8 (3), 243-281.

Taboada, Maite and William C. Mann. 2006. Rhetorical Structure Theory: Looking back and moving ahead. Discourse Studies, 8 (3), 423-459.
http://www.sfu.ca/rst/
http://www.sfu.ca/rst/05bibliographies/

Kamp, H. & Reyle, U. (1993): From Discourse to Logic: Introduction to Model-theoretic Semantics of NaturalLanguage?, Formal Logic and Discourse Representation Theory, Kluwer Academic Publishers, Dordrecht

Kamp, H. van Grenabith, J. & Reyle, U., Representation Theory Discourse ,
http://www.springerlink.com/content/q487665206465365
Mann, William C. and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8 (3), 243-281.

Taboada, Maite and William C. Mann. 2006. Rhetorical Structure Theory: Looking back and moving ahead. Discourse Studies, 8 (3), 423-459.
http://www.sfu.ca/rst/
http://www.sfu.ca/rst/05bibliographies/

Kamp, H. & Reyle, U. (1993): From Discourse to Logic: Introduction to Model-theoretic Semantics of NaturalLanguage?, Formal Logic and Discourse Representation Theory, Kluwer Academic Publishers, Dordrecht

Kamp, H. van Grenabith, J. & Reyle, U., Representation Theory Discourse ,
http://www.springerlink.com/content/q487665206465365

Penn Discourse Treebank, http://www.seas.upenn.edu/~pdtb/

Inderjeet Mani, James Pustejovsky & Rob Gaizauskas (eds), The Language of Time, Oxford University Press

1. Oral examination
2. A contribution on an argument related to the topics discussed in the lectures or, in alternative, an annotation exercise on a corpus using an annotation tool. Evaluation and validation of the annotation scheme.