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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Natural Language Understanding (Level 11) (INFR11061)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
Summary**Replaced with new course Natural Language Understanding, Generation, and Machine Translation (INFR11157).**

This course explores current research into interpreting natural language. Motivations for this study range from foundational attempts to understand how people interpret communication to entirely practical efforts to engineer systems for performing a variety of language tasks, such as information extraction, question answering, natural language front ends to databases, human-robot interaction and customer relationship management, to name a few.

This course represents an introduction to the theory and practice of computational approaches to natural language understanding. The course will cover common parsing methods for sentences, discourse and dialogue, and it will also address lexical processing tasks such as word sense disambiguation and clustering. We will study state of the art symbolic techniques in deep and shallow language processing, as well as statistical models, acquired by both unsupervised and supervised machine learning from online linguistic resources. Students will have the opportunity to explore what they have learned in written and practical assignments. These assignments will be designed to enable students to gain an understanding for the pervasiveness of language ambiguity at all levels and the problems this poses for automated language understanding, and for the relative strengths and weaknesses of the various theories and engineering approaches to these problems.
Course description Parsing
* Advanced parsing models; e.g., headed PCFGs
* Grammar Induction
* Discriminative Parsing
* Shallow parsing
* Human models of sentential parsing (e.g., incrementality)

Semantic Processing
* Semantic Construction in wide-coverage online grammars
* Word sense disambiguation
* clustering, similarity distributions
* lexical subcat acquisition and semantic role labelling
* Human models of lexical processing (e.g., semantic priming)

* Anaphora resolution
* Discourse segmentation
* Dialogue act recognition
* Discourse parsing (including learning discourse structure)
* Human models of discourse and dialogue (e.g., the alignment model)
* Advanced topics

Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Natural Language Computing
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Accelerated Natural Language Processing (INFR11125) OR ( Informatics 2A - Processing Formal and Natural Languages (INFR08008) AND Foundations of Natural Language Processing (INFR09028))
Co-requisites Students MUST also take: Introductory Applied Machine Learning (INFR10063) OR Introductory Applied Machine Learning (INFR10069) OR Machine Learning & Pattern Recognition (Level 11) (INFR11073) OR Machine Learning and Pattern Recognition (INFR11130)
Prohibited Combinations Other requirements Programming skills at least at the level of Computer
Programming for Speech and Language Processing are also required.

This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Students should be able to use and explain appropriate state-of-the-art symbolic parsing techniques, and, where a labelled corpus is available, statistical parsing techniques (generative and discriminative)
  2. Given an NLU system, students should be able to choose appropriate evaluation metrics for the system, use error analysis to propose improvements, and relate it to features of human models of language interpretation at various levels of processing
  3. Given an example of a problem in coreference resolution, discourse segmentation, and discourse parsing, students should be able to provide a written description of how current symbolic and statistical techniques help solve the problem
  4. Given a model and a labelled corpus, students should be able to employ existing ML software packages to train the model on the corpus in order to perform a lexical semantic task
  5. Given an open-ended problem of choosing informative features for a particular NLP task and a description of the available training resources, the student should be able to give a well-justified, written and/or practical, selection of such informative features
Reading List
Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by Daniel Jurafsky and James Martin, Pearson Prentice Hall, 2nd Edition 2008
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Adam Lopez
Tel: (0131 6)50 4430
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
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