| CODE | ARI2572 | |||||||||
| TITLE | Computational Morphology and Syntax | |||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | |||||||||
| MQF LEVEL | 5 | |||||||||
| ECTS CREDITS | 6 | |||||||||
| DEPARTMENT | Artificial Intelligence | |||||||||
| DESCRIPTION | This study-unit consists of two parts, one on computational morphology and one on computational syntax. In the first part, the student will be given an overview of relevant computational models for the analysis of morphological phenomena. In addition, the unit will focus on the analysis of word formation processes in one particular language and its implementation using computational techniques to analyse or generate word forms. Similarly, in the second part of the unit different approaches to automatic syntactic analysis will be presented, including the most influential approaches to rule-based parsing, as well as inductive parsing. Text fragments from a particular language will be parsed using at least one of the approaches, and the results evaluated. In discussing these methods, students will be introduced to fundamental concepts in the formal analysis of grammar, especially the concepts underlying finite state methods and regular expressions, as well as grammar formalisms of varying complexity, such as context-free and context-sensitive formalisms. Study-Unit Aims: This study-unit aims to assist students to: - Get acquainted with foundation tools in NLP, with particular emphasis on morphological analysers and parsers; - Analyse how they are built and the type of approaches possible; - Get acquainted with the most important models in the analysis of word formation and syntactic structure; This study-unit also aims to: - Provide an implementation in a computational framework; - Identify some possible computational applications of morphological and syntactic analysis. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Provide a formally precise analysis of different morphological processes and syntactic constructions; - Interpret a formal model of morphological processes procedurally, with a view to implementing it computationally; - Describe the basic mechanisms of rule-based parsing and inductive parsing and how they relate to the syntactic complexity of natural language; - Describe specific parsing algorithms with a view to implementing them computationally; - Evaluate parsing results according to a formal evaluation method. 2. Skills: By the end of the study-unit the student will be able to: - Provide a computational implementation capable of morphological and/or syntactic analyses of chosen examples; - Use relevant software to this end; - Formalize relevant linguistic phenomena. Main Text/s and any supplementary readings: Jurafsky, D. & J. H. Martin (2009). Speech and Language Processing. (2nd edition). Indiana: Prentice Hall. Roark, B. and R. Sproat (2007). Computational Approaches to Morphology and Syntax. Oxford University Press. Kubler, S., McDonald, R., and Nivre, J. (2009). Dependency Parsing. Morgan & Claypool Publishers. Bird, S., E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O'Reilly Media, 2009. |
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| ADDITIONAL NOTES | Pre-requisite Qualifications: Basic knowledge of grammar, formal theory of grammar and programming. | |||||||||
| STUDY-UNIT TYPE | Ind Study, Lecture, Ind Online Learning & Project | |||||||||
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| LECTURER/S | Claudia Borg |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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