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【全英课程】开授全英课程《Emerging Topics in Text Processing》

来源:计算机科学与技术学院 Admin   发布日期: 2019-11-04 11:05:33

    为了进一步引进国外先进教育理念和优质教育资源,促进全英语课程的建设,在国际交流与合作处的帮助下,我学院邀请了来自悉尼科技大学霍欢博士。将为我们教授全英文的课程《Emerging Topics in Text Processing》。此课程是学生开题培训的一个环节,请同学们积极参加。


课程简介:

Learning Objective

    Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English). It includes a number of AI areas, such as text understanding and summarization, machine translation, sentiment analysis and privacy issues. This subject introduces the foundations of technologies in NLP and their application to practical problems. It brings together the state-of-the-art research and practical techniques in NLP, providing students with the knowledge and capacity to conduct NLP research and to develop NLP projects.


具体安排如下:

  

时间:2019年11月6日――2019年11月8日,2019年11月11日;

地点:上海电力学院临港校区 计电楼2楼201会议室(暂定);

对象:电力信息技术专业/计算机技术专业2018级和2019级全体研究生+感兴趣的相关老师。

报名:11月6日前邮件报名至杜老师(haizhou.du@shiep.edu.cn)。

考勤:每次上课均视为参与讲座一次,按讲座格式盖章。


Content (Total 32 hours),地点: 计电楼2楼201会议室

Lecture 1 (4 hours)

11月6日上午8:00-12:00 Overview of AI Trend

Workshop1 (4 hours)

11月6日下午1:00-5:00 Innovation CASE study with TRIZ

Lecture 2 (4 hours)

11月7日上午8:00-12:00 Intro to Knowledge Graph

Seminar 1 (4 hours)

11月7日下午1:00-5:00 How to conduct high impact research

Lecture 3 (4 hours)

11月8日上午8:00-12:00 Intro to Natural Language Processing

Seminar 2 (4 hours)

11月8日下午1:00-5:00 How to write high level papers

Lecture 4 (4 hours)

11月11日上午8:00-12:00 Intro to Privacy Issues in NLP 

Seminar 3 (4 hours)

11月11日下午1:00-5:00 Assessment session


Learning Outcome

Upon successful completion of this subject students should be able to:

1.Explain the advantages and disadvantages of different NLP technologies and their applicability in different business situations.

2.Use NLP and KG technologies to explore and gain a broad understanding of text data.

3.Use NLP and KG methods to analyse sentiment of a text document.

4.Use NLP and KG methods to perform topic modelling.

5.Organise and implement a NLP project in a business environment.

6.Interpret the results of a NLP project.


Student Assessment

Continuous Assessment Assignments     

Group Study           50%

Final project report    50%

Total              100%

Grading: Pass/Fail 


计算机科学与技术学院

2019年11月4日


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