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2019　Language Engineering

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Academic unit or major
Graduate major in Information and Communications Engineering
Instructor(s)
Okumura Manabu 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(G223)  Thr7-8(G223)  
Group
-
Course number
ICT.H508
Credits
2
Academic year
2019
Offered quarter
2Q
Syllabus updated
2019/6/11
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

The following are the topics in the course: information retrieval, information extraction, text summarization, question answering, sentiment analysis, and text mining.

We will study natural language processing technologies that can understand natural languages, and text processing technologies that can be one of their applications.

Student learning outcomes

To understand what natural language processing technologies are, and to grasp the latest technological trends in text processing technologies, such as information retrieval, information extraction, text summarization, question answering, sentiment analysis, and text mining.

Keywords

Natural Language Processing, Information Retrieval, Link Analysis, Text Classification, Information Extraction, Text Summarization, Question Answering, Text Mining, Social Media

Competencies that will be developed

✔ Specialist skills Intercultural skills ✔ Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

In the first half of the course, basics of each topic are given. In the latter half, recent papers of each topic are read in turns. Students are asked to read a paper of your interest and give a presentation on it in class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction To understand what natural language processing technologies are
Class 2 Fundamentals of Information Retrieval To understand how information retrieval systems can be implemented
Class 3 Advanced Indexing with Morphological, Syntactic, and Semantic Analysis To understand how natural language processing technologies can contribute to information retrieval
Class 4 Query Expansion with Dictionaries and Corpora To understand what query expansion technologies are in information retrieval
Class 5 Link Analysis To understand what link analysis is
Class 6 Text Classification To understand what text classification is
Class 7 Information Extraction To understand what information extraction is
Class 8 Text Summarization 1: Basic Methods To understand what text summarization is
Class 9 Text Summarization 2: Advanced Topics to grasp the latest technological trends in text summarization
Class 10 Question Answering To understand what question answering is
Class 11 Sentiment Analysis To understand what sentiment analysis is
Class 12 Text Mining 1: From the Perspective of Database Engineering To understand what text mining is from database engineering
Class 13 Text Mining 2: From the Perspective of Language Processing To understand what text mining is from language processing
Class 14 Text Mining 3: Social Media and Text Mining to grasp the latest technological trends in text mining
Class 15 Applications To discuss the future of language processing technologies and text processing technologies

Textbook(s)

No textbook

Reference books, course materials, etc.

Course materials are provided during class.

Assessment criteria and methods

Presentation: 50%, final reports: 50%

Related courses

  • ICT.H410 ： Computational Linguistics
  • ICT.H503 ： Speech Information Technology
  • ICT.H318 ： Foundations of Artificial Intelligence (ICT)
  • ICT.H212 ： Automata and Languages (ICT)

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None required

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