Subject

XSL Content

Computational syntax

General details of the subject

Mode
Face-to-face degree course
Language
English

Description and contextualization of the subject

This course will present the main approaches to the computational treatment of syntax. Among others, contex-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.

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
BARNES , JEREMY CLAUDEUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualComputer Languages and Systemsjeremy.barnes@ehu.eus
GOJENOLA GALLETEBEITIA, KOLDOBIKAUniversity of the Basque CountryProfesorado Titular De UniversidadDoctorBilingualComputer Languages and Systemskoldo.gojenola@ehu.eus
URIZAR ENBEITA, RUBENUniversity of the Basque CountryProfesorado Titular De UniversidadDoctorBilingualTeaching of Language and Literature ruben.urizar@ehu.eus

Competencies

NameWeight
Ability to handle, enrich and use language resources for the processing of human language.20.0 %
Understanding of the basic strategies for the analysis of language, and capacity of extending these strategies for their use in applications for language processing.20.0 %
Ability to use and adapt the tools (morphological, syntactic and semantic analyzers) available for different languages.20.0 %
Ability to design and develop resources, tools and computer applications for language technologies.20.0 %
Ability to use and adapt the relevant methods for research on language technologies.20.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Lecture-based1522.537.5
Applied computer-based groups304575

Training activities

NameHoursPercentage of classroom teaching
Lectures37.540 %
Prácticas con ordenador, laboratorio, salidas de campo, visitas externas75.040 %

Assessment systems

NameMinimum weightingMaximum weighting
Attendance and participation5.0 % 5.0 %
Drawing up reports and presentations75.0 % 75.0 %
Written examination20.0 % 20.0 %

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. Multi-word expressions (MWE) 4. Context-free grammars 4.1. Basic model 4.2. Probabilistic Context-free Grammars 4.3. Unification-based grammars 5. Dependency syntax 5.1. Rule-based5.2. 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

• Chomsky, N. (1957). Syntactic structures. The Hague: Mouton. • R Socher, J Bauer, CD Manning. Parsing with compositional vector grammars Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics 2013 • Association for Computational Linguistics (ACL) Anthology: