Postgraduate Course: Advanced Natural Language Processing (INFR11059)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|Course description||The 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)
|Prohibited Combinations|| Students MUST NOT also be taking
Foundations of Natural Language Processing (INFR09028)
||Other requirements|| 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.
Programming at the level of Computer Programming for Speech and Language Processing (LASC11096) or equivalent.
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||Yes
Course Delivery Information
|Delivery period: 2013/14 Semester 1, Available to all students (SV1)
||Learn enabled: No
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Lecture Hours 30,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
||Hours & Minutes
|Main Exam Diet S1 (December)||Advanced Natural Language Processing||2:00|
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.
|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.
||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
||* Jurafsky and Martin, Speech and Language Processing, 2nd edition, 2008.
* D. Harel Algorithmics.
Timetabled Laboratories 0
Non-timetabled assessed assignments 50
Private Study/Other 30
|Course organiser||Dr Iain Murray
Tel: (0131 6)51 9078
|Course secretary||Ms Katey Lee
Tel: (0131 6)50 2701
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am