Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	354664	Natural Language Processing and Text Mining	1	3.0	3	★	Jenq-Haur Wang	4CSIE3	46	0

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2025-12-17 16:26:21
Course Objective	在各種應用情境中，非結構化的文件相當普遍。如何精確有效地從文件中擷取出重要資訊並分析語意，將有助於理解與彙整不同想法。本課程介紹基本的自然語言處理技術，包括: 語彙組成、語法分析、語意分析等，以及常見的文件探勘方法。除了傳統的詞性標註 (POS tagging)、關鍵詞擷取 (keyterm extraction)、中文斷詞 (word segmentation)等任務之外，也將探討不同的字詞與文件表達方式，並應用深度學習及大型語言模型進行有效的語意分析，如: 情緒分析、意見探勘等。
Course Schedule	1. Course Overview (1 wk) Part I: Natural Language Processing Weeks (2-9) 2. Words and Tokens 3. N-gram Language Models 4. Logistic Regression and Text Classification 5. Embeddings 6. Neural Networks 7. Large Language Models 8. Transformers 9. Information Retrieval and Retrieval-Augmented Generation 10. Midterm Part II: Text Mining Weeks (11-15) 11. Word embeddings 12. Phrase mining 13. Named entity recognition 14. Pattern-based text mining 15. Taxonomy mining 16-18. Term Project Presentation
Evaluation and grading policy	- Homework and Programming Exercises: ~40% - Mid-term exam: ~30% - Term Project: ~30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: * Daniel Jurafsky and James H. Martin. 2025. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3rd edition. Online manuscript released August 24, 2025. https://web.stanford.edu/~jurafsky/slp3. References: * Christopher Manning and Hinrich Schuetze, Foundations of Statistical Natural Language Processing, MIT Press, 1999. - https://nlp.stanford.edu/fsnlp/ * James Allen, Natural Language Understanding, 2nd ed., Benjamin Cummings, 1994. * Stanford Course CS224N: Natural Language Processing with Deep Learning * UIUC Course CS447: Natural Language Processing * UIUC Course CS512: Data Mining: Principles and Algorithms * Related academic papers
The access to curricular consultation	The teacher can be reached via e-mails or messages in Microsoft Teams. The TA will help you join the course group in Facebook where you can also ask questions about the homeworks and exams.
Expanding teaching and resources
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	1. 課程相關訊息將透過以下管道公布: 北科i學園、Teams、或課程網頁: https://chriswjh.github.io/NLP_TM/。 2. 若因特殊情形無法到校，請使用Teams遠距上課（帳號: 學號@cc.ntut.edu.tw）。 3. 如果你開學後尚未被加入本課程之Teams團隊，請來信: jhwang@ntut.edu.tw，並註明課程名稱與你的班級、姓名。 4. 待助教建立課程Facebook群組後，相關訊息公告或問題諮詢，也可以在課程Facebook群組詢問。