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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Baldwin Tim Teufel Simone Heidi
- Class Format
- Lecture
- Media-enhanced courses
- 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
| ✔ Specialist skills | Intercultural skills | Communication 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
