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開講年度 2019年度 開講箇所 グローバルエデュケーションセンター
The Application of Artificial Intelligence β: Natural Language Processing


担当教員 朝日 透/尾形 哲也/谷口 卓也/ジョーンズ ベーベン キーリ/江原 遥
学期曜日時限 集中講義(春学期)  土1-2
科目区分 ユニバーシティ・スタディーズ科目 配当年次 1年以上 単位数 1
使用教室 ホール キャンパス 日本橋
科目キー 9S91010036 科目クラスコード 01
授業で使用する言語 英語
  コース・コード INFI616S
大分野名称 情報学
中分野名称 知的システム
小分野名称 一般
レベル 修士レベル 授業形態 演習/ゼミ


最終更新日時:2019/04/01 10:26:38


The class will introduce several centraltopics of NLP, with an emphasis on current research at AIST in naturallanguage understanding.

The state of the art relies on a mix ofparameter estimation, algorithmic search, and the novel application of languagemodeling formalisms such as grammars and recurrent neural networks,

and the course will touch on all threeingredients, equipping students with a knowledge of some of the currently mostimportant approaches.

Students taking the class are expectedto already have knowledge of the basics of AI.

授業の到達目標 Students should have a grasp of the fundamentals of classic topics in NLP including part of speech tagging, named entity recognition, syntactic and semantic parsing, machine translation, and question answering, and, with effort, be able to follow current developments on the cutting edge of the field.
事前・事後学習の内容 Read pre-work items, which are sent by faculties, in 1 hour before each lecture. The report about each lecture should be submitted to faculties at the classroom or through Course-N@vi system.

1st  June 8/9:00-10:30, Orientation, Takuya Taniguchi

2nd June 8/10:40-12:10, Language model, Bevan Keeley Jones, Takuya Taniguchi

3rd  June 15/9:00-10:30, Syntactic parsing, Bevan Keeley Jones, Takuya Taniguchi

4th June 15/10:40-12:10, Semantic parsing, Bevan Keeley Jones, Takuya Taniguchi

5th June 29/9:00-10:30, Neural machine translation, Yo Ehara, Takuya Taniguchi

6th  June 29/10:40-12:10, Grammatical error correction, Yo Ehara, Takuya Taniguchi

7th July 6/9:00-10:30, Educational NLP & NLP Applications, Yo Ehara, Takuya Taniguchi

8th  July 6/10:40-12:10, Exam, Takuya Taniguchi

割合 評価基準
試験: 40% In the final day, the students must take the examination to get the credit.
レポート: 30% The students must submit the report for issues, which are indicated by faculty members, to them.
平常点評価: 20% The on-site contribution to the class is evaluated.


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