2019 Advanced Topics in Computing C

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Academic unit or major
School of Computing
Instructor(s)
Baldwin Tim  Teufel Simone Heidi 
Course component(s)
Lecture
Day/Period(Room No.)
-
Group
-
Course number
XCO.T673
Credits
2
Academic year
2019
Offered quarter
2Q
Syllabus updated
2019/5/31
Lecture notes updated
2019/9/25
Language used
English
Access Index

Course description and aims

he web has rapidly become the default medium for publishing and disseminating information, particularly via social media platforms (encompassing blogs, forums, Q&A sites, microblogs, and content sharing/recommender services), and with a strong focus on textual and image/video data. The volume, veracity, and
variety of that data is ever increasing, creating a need for AI technologies that assist users in accessing and making sense of data that is of pertinent to them and contexualised appropriately. In this subject, we will introduce a range of such AI technologies situated across a breadth of web sources and applications, including single and multi-document summarisation, document filtering, user/document geolocation, stance prediction, question matching, answer aggregation/ranking, and user demographic prediction/debiasing.

This course is for Doctoral Students with an interest in NLP. The ideal participant in this course already has some background in general NLP, but beginners in NLP are also welcome. The topic covered concerns many technologies that go beyond basic NLP. However, all background information that is required to understand the main concepts treated in this course will be summarised in the first set of lectures+activities.

Student learning outcomes

Students will become acquainted with web-related AI technologies, such as summarisation, question answering, sentiment detection, document filtering, question matching, answer ranking and aggregation and more. Students will experience the various methods themselves in first-hand in exercises, for instance by performing annotation exercises on the real text. This way, they will get a real-world encounter with the tasks and texts that automatic methods face when providing access to the web resources, rather than just seeing idealised examples chosen for illustrative purposes in the literature.
As a result, students who directly work on related research should be able to put the new knowledge from this course into practice by creating more sophisticated automatic treatments of their chosen tasks. But students not directly working on NLP should also benefit. Background knowledge about AI-web technology should help them with the design of a range of multi-modal applications. All students, whatever their exact subjects, could also profit by learning to design more meaningful evaluations of their systems.

Keywords

Web resources, text understanding, artificial intelligence, natural language processing

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Students will be required to read one paper (8-15 pages) per topic (6 papers in total). Because the course is intensive, they should ideally do some of this reading before the course starts. Reading will be provided well ahead of schedule.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Lecture 1 Basics of NLP and IR Specified in the class
Class 2 Activity 1 Basics of NLP and IR
Class 3 Lecture 2 Text Summarisation
Class 4 Activity 2 Text Summarisation
Class 5 Lecture 3 Citation Processing and Search
Class 6 Activity 3 Citation Processing and Search
Class 7 Evaluation 1
Class 8 Lecture 4 NLP for social media: document preprocessing
Class 9 Activity 4 NLP for social media: document preprocessing
Class 10 Lecture 5 NLP for social media: semantics and discourse
Class 11 Activity 5 NLP for social media: semantics and discourse
Class 12 Lecture 6 NLP for social media: socially-situated NLP
Class 13 Activity 6 NLP for social media: socially-situated NLP
Class 14 Evaluation 2

Textbook(s)

None

Reference books, course materials, etc.

Specified in the class

Assessment criteria and methods

An exam will be conducted on all topics covered in the course.

Related courses

  • ART.T458 : Machine Learning
  • ART.T459 : Natural Language Processing
  • ART.T462 : Complex Networks
  • ART.T464 : Information Organization and Retrieval

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

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