2019年度 情報理工学特別講義C   Advanced Topics in Computing C

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開講元
情報理工学院
担当教員名
BALDWIN TIMOTHY JOHN  TEUFEL SIMONE HEIDI 
授業形態
講義
曜日・時限(講義室)
-
クラス
-
科目コード
XCO.T673
単位数
2
開講年度
2019年度
開講クォーター
2Q
シラバス更新日
2019年5月31日
講義資料更新日
2019年9月25日
使用言語
英語
アクセスランキング

講義の概要とねらい

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.

到達目標

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.

キーワード

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

学生が身につける力

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
- - -

授業の進め方

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.

授業計画・課題

  授業計画 課題
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7回 Evaluation 1
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
第14回 Evaluation 2

教科書

None

参考書、講義資料等

Specified in the class

成績評価の基準及び方法

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

関連する科目

  • ART.T458 : 機械学習
  • ART.T459 : 自然言語処理
  • ART.T462 : 複雑ネットワーク
  • ART.T464 : 情報の組織化と検索

履修の条件(知識・技能・履修済科目等)

None

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