Description and contextualization of the subject

This course will present the main approaches to the computational treatment of syntax. Among others, context-free grammars, finite state syntax, and statistical models. Various formalisms will be presented, such as context-free grammars, probabilistic grammars, unification grammars, and constraint grammar. We will also present a main overview of morphosyntactic tagging (Part of Speech tagging), partial recognition (chunking), and parsing.

An overview will be given of the role of morphology and syntax in applications using language technology.

Learning outcomes of the subject

Know the basic elements of computational syntax: (a) context-free grammars, (b) probabilistic grammars, (c) unification grammars, (d) constituent-based syntax and (e) dependency syntax.

Learn to write code and use libraries for the development of parsers in NLP.

Implementation of specific tasks of computational syntactic analysis with autonomy.

Temary

1 Introduction to Computational Syntax



2. Finite state syntax

2.1. Syntactic category assignment (POS tagging)

Knowledge-based (Constraint Grammar)

Data-driven (statistical methods)

2.2. Chunking



3. Context-free grammars

3.1. Basic model

3.2. Probabilistic Context-free Grammars

3.3. Unification-based grammars



4. Dependency syntax

4.1. Rule-based

4.2. Data driven

Bibliography

Basic bibliography

• Jacob Eisenstein. Introduction to Natural Language Processing, MIT Press Ltd, Cambridge, United States, ISBN10 0262042843, ISBN13 9780262042840



• Brian Roark and Richard Sproat. 2007. Computational Approaches to Morphology and Syntax. Oxford University Press



• D. Jurafsky, James H. Martin. Speech and Language Processing (Second Edition), Prentice Hall, Upper Saddle River, N.J. , 2008.

• Noah A. Smith (2011) Linguistic Structure Prediction. Morgan & Claypool Publishers, Synthesis Lectures on Human Language Technologies May 2011, Vol. 4, No. 2



• C. Manning, H. Schütze (1999) Foundations of Statistical Natural Language Processing, MIT Press Cambridge, Mass., 1999.



• Robert B. Kaplan (ed) The Oxford Handbook of Applied Linguistics Second Edition Edited by OUP USA Oxford Handbooks in Linguistics, 2010.



• Alexander Clark, Chris Fox, Shalom Lappin (eds.), The Handbook of Computational Linguistics and Natural Language Processing. Blackwell Handbooks in Linguistics, John Wiley & Sons, 2012.



• R. Dale, H. Moisl, H.Somers (ed.), Handbook of Natural Language Processing. Marcel Dekker, New York, 2000.



• Hopcroft J., Motwani R., Ullman J., Introduction to automata theory, languages and computation. Pearson-Addison Wesley, 2001.



• Nitin Indurkhya, Fred J. Damerau, Handbook of Natural Language Processing, Second Edition. Chapman & Hall/CRC Machine Learning & Pattern Recognition, 2010.



• Sproat R. Computational Morphology. 1992. ACL-MIT Series in Natural Language Processing.



• Sipser, M. (2012). Introduction to the Theory of Computation. Cengage Learning.

In-depth bibliography

- Jacob Eisenstein. Natural Language Processing. 2019. the MIT Press.







- Brian Roark and Richard Sproat. 2007. Computational Approaches to Morphology and Syntax. Oxford University Press.







- D. Jurafsky, James H. Martin. Speech and Language Processing (Second Edition), Prentice Hall, Upper Saddle River, N.J. , 2008.







- C. Manning, H. Schütze (1999) Foundations of Statistical Natural Language Processing, MIT Press Cambridge, Mass., 1999.







- Bird S., Ewan Klein, and Edward Loper. Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit (2009). O'Reilly Media. http://www.nltk.org/book/







- Yoav Goldberg, Graeme Hirst. Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies). 2017. Primer. http://u.cs.biu.ac.il/~yogo/nnlp.pdf







- Chomsky, N. (1957). Syntactic structures. The Hague: Mouton.