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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Advanced Natural Language Processing (INFR11059)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website Taught in Gaelic?No
Course descriptionThe course will synthesize recent research in linguistics, computer science, and natural language processing with the aim of introducing students to theoretical and computational models of language. The course will familiarize students with a wide range of linguistic phenomena with the aim of appreciating the complexity, but also the systematic behaviour of natural languages like English, the pervasiveness of ambiguity, and how this presents challenges in natural language processing. In addition, the course introduce the most important algorithms and data structures that are commonly used to solve many NLP problems.

The course will cover formal models for representing and analyzing syntax and semantics of words, sentences, and discourse. Students will learn how to analyse sentences algorithmically, using hand-crafted and automatically induced treebank grammars, how to make monotonic syntactic derivations, and build interpretable semantic representations. The course will also cover a number of standard algorithms that are used throughout language processing. Examples include Hidden Markov Models, the EM algorithm, and state space algorithms such as dynamic programming.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Foundations of Natural Language Processing (INFR09028)
Other requirements For Informatics PG and final year MInf students only, or by special permission of the School.

CPSLP or equivalent background..

Additional Costs None
Information for Visiting Students
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2011/12 Semester 1, Available to all students (SV1) WebCT enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 09:00 - 09:50
CentralLecture1-11 09:00 - 09:50
CentralLecture1-11 09:00 - 09:50
First Class Week 1, Wednesday, 09:00 - 09:50, Zone: Central. Seminar Room 2, Crystal Macmillan Building
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S1 (December)Advanced Natural Language Processing2:00
Summary of Intended Learning Outcomes
1 - Students should be able to construct examples of ambiguous Natural Language sentences and provide a written explanation of how ambiguity arises in natural language and why this is a problem for computational analysis.
2 - Given a grammar, semantics and sentence, students should be able to construct a syntatic and semantic analysis of the sentence.
3 - Given an appropriate NLP problem, students should be able to apply sequence models, parsing and search algorithms and provide a summary of their operation in this context.
4 - Given an appropriate NLP problem, students should be able to analyse the problem and decide which data structures and algorithms to apply. ? Review and classify search algorithms and ways of manipulating dynamic data structures.
5 - Given two NLP algorithms, students should be able to describe how they are related and illustrate differences and limitations by providing illustrative examples.
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0

There will be three coursework exercises; one on sequence models, one on parsing, and one on applying the methods introduced in the course to an unseen problem.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Special Arrangements
Additional Information
Academic description Not entered
Syllabus Part I: Words
* Inflectional and derivational morphology
* Finite state methods and Regular expressions
* Word Classes and Parts of speech
* Sequence Models (Markov and Hidden Markov models, smoothing)
* The Viterbi algorithm, Forward Backward, EM
* Maximum Entropy Models

Part II: Syntax
* Language and Complexity (e.g., Chomsky hierarchy, the Pumping Lemma)
* Syntactic Concepts (e.g., constituency, subcategorisation, bounded and unbounded dependencies, feature representations)
* Analysis in CFG - Greedy algorithms---Shift-reduce parsing
* Divide-and-conquer algorithms---CKY
* Chart parsing
* Lexicalised grammar formalisms (e.g., TAG, CCG, dependency grammar)
* Trans-CF grammars
* Statistical parsing (PCFGs, dependency parsing)
* Search algorithms: Breadth-first, depth-first search, A* search
* Minimum spanning trees for dependency parsing

Part III: Semantics and Pragmatics
* logical semantics and compositionality
* Semantic derivations in grammar
* Lexical Semantics (e.g., word senses, semantic roles)
* Discourse (e.g., anaphora, speech acts, )

Part IV: Corpus creation and Evaluation
* Markup, annotation
* Evaluation measures

Relevant QAA Computing Curriculum Sections: Not yet available
Transferable skills Not entered
Reading list * Jurafsky and Martin, Speech and Language Processing, 2nd edition, 2008.
* D. Harel Algorithmics.
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 50
Private Study/Other 30
Total 100
KeywordsNot entered
Course organiserDr Michael Rovatsos
Tel: (0131 6)51 3263
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
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