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.
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.
Web resources, text understanding, artificial intelligence, natural language processing
|国際的教養力||コミュニケーション力||✔ 専門力||課題設定力||✔ 実践力または解決力|
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.
|第1回||Lecture 1 Basics of NLP and IR||講義において指定する|
|第2回||Activity 1 Basics of NLP and IR|
|第3回||Lecture 2 Text Summarisation|
|第4回||Activity 2 Text Summarisation|
|第5回||Lecture 3 Citation Processing and Search|
|第6回||Activity 3 Citation Processing and Search|
|第8回||Lecture 4 NLP for social media: document preprocessing|
|第9回||Activity 4 NLP for social media: document preprocessing|
|第10回||Lecture 5 NLP for social media: semantics and discourse|
|第11回||Activity 5 NLP for social media: semantics and discourse|
|第12回||Lecture 6 NLP for social media: socially-situated NLP|
|第13回||Activity 6 NLP for social media: socially-situated NLP|
Specified in the class
An exam will be conducted on all topics covered in the course.