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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Topics in Natural Language Processing (INFR11113)

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
SummaryThe aim of this course is to expose students to a variety of advanced topics in computational linguistics and natural language processing (NLP). Most of these topics will be described through the introduction of the basic theory that underlies these topics. There is an important theoretical component in NLP and CL that students are not being normally exposed to - the goal of the class is to increase the exposure to these topics. The course will focus especially on learning-theoretic and formal language theoretic issues in NLP. Students will be expected to present and critique classic and recent research results articles from the NLP literature, chosen from a list provided by the instructor.
Course description The syllabus consists of two parts. In the first part, the instructor will provide a basic overview of topics that will be covered in the class, and some of the fundamental ideas in NLP. She or he will also discuss methodologies to read and analyse scientific papers on topics in NLP. In the second part, students will give the presentations and brief paper responses. The papers will be chosen from a list provided by the instructor (or suggested by the students, subject to the instructor's approval).

Topics covered by the instructor will include:
*A refresher in probability and information theory.
*Basic introduction to formal language theoretic tools used in NLP such as finite state transducers, advanced grammar topics.
*Introduction and overview of advanced statistical modelling techniques in NLP such as structured prediction, log-linear models.
*Information about reading and analysing research papers in NLP.

Relevant QAA Computing Curriculum Sections:
Artificial intelligence; Natural language computing
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Foundations of Natural Language Processing (INFR09028) OR Accelerated Natural Language Processing (INFR11125)
It is RECOMMENDED that students have passed Information Theory (INFR11087) OR Introductory Applied Machine Learning (INFR10063) OR Introductory Applied Machine Learning (INFR10069)
Prohibited Combinations 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.

This course requires familiarity with probability theory and machine learning, at the level of, e.g., Introduction to Applied Machine Learning or Information Theory
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 15, Feedback/Feedforward Hours 2, Summative Assessment Hours 3, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) The assessment for this course consists of the following components:

*Oral presentation (20%): students (in small groups perhaps, depending on enrollment) will choose 1-3 papers on a specific topic (topics can be repeated) from a list provided by the instructor. Freedom will be given to students to choose a topic, if they show they are able to grasp the material and present it well.
*Brief paper responses (15%): Students will choose 1-2 additional papers (on the same topic as their presentation), and will write a short summary on the topic and these papers for all other students to read (1 page). This summary will also suggest ideas to further expand the research presented in these papers, or questions that students had during their readings.
*Assignment (10%): Students will solve a pencil-and-paper assignment that covers the first part of the lecture. This assignment is aimed at assessing what students learned in the first part of the course.
*Essay (55%): This is the final piece of assessment, in which students could choose a potentially new topic (or stick to the old topic of their presentation and the brief paper responses), and create a literature review of this topic, or an essay based for this topic.

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.
Feedback Feedback will be provided in the following ways: (a) the lecturer will comment on each individual student's presentation both in terms of the delivery and content, (b) the lecturer will provide detailed instructions on how the final essay must be structured and examples of what makes a good essay; the essay will be marked against the lecturer's guidelines and the students will receive feedback on several dimensions (e.g. organisation, writing, ideas, understanding of the material and so on). It is envisaged that the students will also learn from their peer group by engaging in dialogue with the course lecturer and their fellow students (e.g., during presentations). (c) the lecturer will provide detailed feedback on the assignment that covers the first part of the course.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate understanding of classic and current articles on statistical modelling of language. Special emphasis on fundamental models such as probabilistic grammars, learning theory for structured prediction and linguistic structure prediction, formal language theory and latent-variable modelling.
  2. Demonstrate understanding of the relationship between NLP and machine learning and statistics, by being able to critically assess the validity and soundness of the techniques used in NLP papers.
  3. Synthesise information from the NLP literature on a specific topic, and create a coherent summary.
  4. Following the above, identifying shortcomings in the literature and suggest solutions for these shortcomings, or ideas for further exploration.
Reading List
There is no main textbook for the course. Readings will be chosen from classic and current research papers in NLP, learning theory and formal language theory.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Shay Cohen
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
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