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Introduction to
Artificial Intelligence for Text Analytics
Artificial Intelligence for Text Analytics
1
Min-Yuh Day, Ph.D,
Associate Professor
Institute of Information Management, National Taipei University https://web.ntpu.edu.tw/~myday 1102AITA MBA, IM, NTPU (M5026) (Spring 2022) Tue 2, 3, 4 (9:10-12:00) (B8F40) 2022 - 02 - 22 https://meet.google.com/ paj-zhhj-mya
Min-Yuh Day, Ph.D.
Associate Professor, Information Management, NTPU
Visiting Scholar, IIS, Academia Sinica
Ph.D., Information Management, NTU
Director, Intelligent Financial Innovation Technology, IFIT Lab, IM, NTPU Artificial Intelligence, Financial Technology, Big Data Analytics, Data Mining and Text Mining, Electronic Commerce
Course Objectives
1. Understand the fundamental concepts and research
issues of Artificial Intelligence for Text Analytics.
2. Equip with Hands-on practices of Artificial Intelligence
for Text Analytics.
3. Conduct information systems research in the context of
Artificial Intelligence for Text Analytics.
Course Outline
- This course introduces the fundamental concepts, research issues, and hands-on practices of Artificial Intelligence for Text Analytics.
- Topics include: 1. Introduction to Introduction to Artificial Intelligence for Text Analytics 2. Foundations of Text Analytics: Natural Language Processing (NLP) 3. Python for Natural Language Processing 4. Natural Language Processing with Transformers 5. Text Classification and Sentiment Analysis 6. Multilingual Named Entity Recognition (NER), Text Similarity and Clustering 7. Text Summarization and Topic Models 8. Text Generation 9. Question Answering and Dialogue Systems 10. Deep Learning, Transfer Learning, Zero-Shot, and Few-Shot Learning for Text Analytics 11. Case Study on Artificial Intelligence for Text Analytics
Four Fundamental Qualities
- Professionalism
- Creative thinking and Problem-solving 40 %
- Comprehensive Integration 40 %
- Interpersonal Relationship
- Communication and Coordination 10 %
- Teamwork 5 %
- Ethics
- Honesty and Integrity 0 %
- Self-Esteem and Self-reflection 0 %
- International Vision
- Caring for Diversity 0 %
- Interdisciplinary Vision 5 %
College Learning Goals
- Ethics/Corporate Social Responsibility
- Global Knowledge/Awareness
- Communication
- Analytical and Critical Thinking
Syllabus Week Date Subject/Topics 1 2022/02/22 Introduction to Artificial Intelligence for Text Analytics 2 2022/03/01 Foundations of Text Analytics: Natural Language Processing (NLP) 3 2022/03/08 Python for Natural Language Processing 4 2022/03/15 Natural Language Processing with Transformers 5 2022/03/22 Case Study on Artificial Intelligence for Text Analytics I 6 2022/03/29 Text Classification and Sentiment Analysis
Syllabus Week Date Subject/Topics 7 2022/04/05 Tomb-Sweeping Day (Holiday, No Classes) 8 2022/04/12 Midterm Project Report 9 2022/04/19 Multilingual Named Entity Recognition (NER), Text Similarity and Clustering 10 2022/04/26 Text Summarization and Topic Models 11 2022/05/03 Text Generation 12 2022/05/10 Case Study on Artificial Intelligence for Text Analytics II
Teaching Methods and Activities
- Lecture
- Discussion
- Practicum
Evaluation Methods
- Individual Presentation 60 %
- Group Presentation 10 %
- Case Report 10 %
- Class Participation 10 %
- Assignment 10 %
Reference Books
- Denis Rothman (2021), Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more, Packt Publishing.
- Savaş Yıldırım and Meysam Asgari-Chenaghlu (2021), Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques, Packt Publishing.
- Sudharsan Ravichandiran (2021), Getting Started with Google BERT: Build and train state-of-the-art natural language processing models using BERT, Packt Publishing.
- Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta (2020), Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems, O'Reilly Media.
Other References
- Dipanjan Sarkar (2019), Text Analytics with Python: A Practitioner’s Guide to Natural Language Processing, Second Edition. APress.
- Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda (2018), Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, O’Reilly.
- Charu C. Aggarwal (2018), Machine Learning for Text, Springer.
- Gabe Ignatow and Rada F. Mihalcea (2017), An Introduction to Text Mining: Research Design, Data Collection, and Analysis, SAGE Publications.
- Aurélien Géron (2019), Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition, O’Reilly Media.
- Frederick Kaefer and Paul Kaefer (2020), Introduction to Python Programming for Business and Social Science Applications, SAGE Publications
- Vic Anand, Khrystyna Bochkay, and Roman Chychyla (2020), Using Python for Text Analysis in Accounting Research, Now Publishers.
Denis Rothman (2021),
Transformers for Natural Language Processing:
Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more , Packt Publishing. Source: https://www.amazon.com/Transformers-Natural-Language-Processing-architectures/dp/1800565798^19
Savaş Yıldırım and Meysam Asgari-Chenaghlu (2021),
Mastering Transformers:
Build state-of-the-art models from scratch with advanced natural language processing techniques, Packt Publishing. Source: https://www.amazon.com/Mastering-Transformers-state-art-processing/dp/1801077657/^20
