Program Specification
|Name of University/Institute King Mongkut's University of Technology Thonburi |
|Faculty/College/Department Computer Science Program, The School of Information Technology |
Section 1 General Information
|Code and Name of Course |
|CSC 494 Special Topic IV – Natural Language Processing |
|Credits |
|3 Credits (3-0-6) |
|Academic Program, Type of Course |
|Bachelor of Science in Computer Science (English Program) |
|Elective Course for Junior and Senior students |
|Responsible faculty and lecturers |
|Assoc. Prof. Jonathan H. Chan, Ph.D., Responsible Lecturer |
|Assoc. Prof. Richard Watson Todd, Ph.D. |
|Stuart G. Towns, Ph.D. (pending) |
|Semester/ year of the program |
|Major elective of CS Juniors and Seniors |
|Pre-requisite (if any) |
|CSC105 Computer Programming II and CSC261 Statistics for Computer Scientists |
|Co-requisite (if any) |
|CSC532 Machine Learning (optional) |
|Place of study |
|School of Information Technology |
|9. Deadline for conducting or improving course specification |
|November 5, 2018 |
Section 2: Aim and Objectives
|1. Course purposes |
|Natural Language Processing (NLP), also commonly referred to as Computational Linguistics, is fast becoming a prominent field in today’s digital |
|society, especially with the advances in artificial intelligence (AI). Students will learn the fundamentals of NLP and understand the kinds of |
|problem that are suitable for NLP. In addition, students will be presented with different techniques in traditional NLP as well as leveraging |
|machine learning and AI techniques such as deep learning concepts. A number of experiments and projects are assigned to provide students with |
|better appreciation of the learnt concepts. |
|2. Course Objectives / Action Objectives |
|Students learn basic concepts in NLP and various data analysis techniques. |
|Students learn how to use software modules and/or software library so that she or he could later use them to solve data analysis problems. |
|3. Objectives of course development and improvement |
|The concept of NLP is relevant to many fields of science, business, and technology. With the widespread availability and thus heavy usage of the |
|world wide web, communication via natural language is essential. Also, the study and understanding of NLP would enhance the students’ knowledge in |
|theoretical computer science, which is useful for learning other related computer science courses and for solving other computer science problems |
|later in his or her studies. |
Section 3: Operation
|Course description |
|This course introduces the fundamental concepts and ideas in NLP and may cover the following topics: introduction to NLP; regular expressions, text|
|normalization, edit distance; N-gram language models; Naïve Bayes and sentiment classification; logistic regression; vector semantics; neural |
|networks and neural language models; part-of-speech tagging; sequence processing with recurrent networks; parsing; representation of sentence |
|meaning; computational semantics; information extraction; lexicons for sentiment; discourse coherence; machine translation; question answering, and|
|dialog systems and chatbots. The course project will cover recent and practical case studies on application of machine learning. |
|Number of hours per semester |
|Lecture |Extra Teaching |Field Experience |Self-Study |
|45 hours per semester |- |- |Self study 6 hours per week |
|3. Number of hours per week of lecturer-provided individual counseling and academic advice. |
|By appointment via email |
Section 4 Learning Outcome Developments
|Morals and Ethics |
|1.1 Morals and Ethics to Be Developed |
|Students should be responsible to society when applying algorithm to solve real-world problems. (1.1.1) |
|Use it with the code of conduct of their profession. Morals and Ethics according to Computer Science program are |
|Discipline, punctuality, responsibility to self and society. (1.1.2) |
|1.2 Teaching Methods |
|Lecture by giving examples and case studies related to morals and ethics |
|Group Discussion |
|Give assignments and projects |
|1.3 Evaluation Methods |
|On-time and completed assignment submission. |
|Evaluate the case studies, assignments and projects based on Morals and Ethics |
|Knowledge |
|2.1 Knowledge to Gain |
|Understand the concept and theories of Natural Language Processing (NLP) (2.1.1) |
|Be able to understand, and analyze the problems and apply appropriate NLP methods to solve such problems. (2.1.2) |
|Gain software development experiences by applying various NLP algorithms into software (2.1.3) |
|Be able to apply NLP algorithms to solve problems in other related fields. (2.1.4) |
|2.2 Teaching Methods |
|Lecture, discussion on case studies, extra reading on recent algorithms, assignments, and projects. The students are also given an opportunity to |
|pass an online MOOC course on Natural Language Processing. |
|2.3 Evaluation Methods |
|Quizzes, midterm and final examinations based on discussed concepts and theories |
|Programming assignments and projects |
|MOOC course completion |
|Cognitive Skills (Wisdom) |
|3.1 Cognitive Skill to Be Developed |
|Be able to thinking systematically (3.1.1) |
|Be able to search, analyze, and evaluate information in order to solve problems. (3.1.2) |
|Be able to gather, study, and summarize the problems and requirements (3.1.3) |
|Be able to apply algorithms and problem solving skills to solve computing problems (3.1.4) |
|3.2 Teaching Methods |
|Group Discussion |
|Assignments |
|Projects |
|3.3 Evaluation Methods |
|Quizzes, midterm and final examinations based on applying NLP techniques to problem solving |
|Assignments. |
|Projects |
|Interpersonal Relationship Skills and Responsibility |
|4.1 Interpersonal Relationship Skills and Responsibility to Be Developed |
|Communicate in English fluently (4.1) |
|4.2 Teaching Methods |
|Class discussion |
|4.3 Evaluation Methods |
|Idea expression and class discussion evaluation |
|Numerical analysis skills, communication skills and using IT |
|5.1 Numerical analysis skills, communication skills and using IT to Be Developed |
|Skill to use NLP algorithms on the assignments (5.1.1) |
|Be able to recommend appropriate solutions using NLP (5.1.2) |
|Write clear, concise, and accurate technical documents following well-defined standards for format and for including appropriate tables, figures, |
|and references. (5.1.3) |
|5.2 Teaching Methods |
|Self study using information from E-Learning and Web. Submit it with actual statistics and reliable references. |
|Homework with proper techniques and technologies |
|5.3 Evaluation Methods |
|Homework evaluation based on techniques and technologies used. |
Section 5 Lesson and Evaluation Plan
| |
|1. Lesson Plan |
|Week |Topics/Details |Hours |learning and teaching |Lecturer |
| | | |activities, teaching media (if| |
| | | |any) | |
|1 |Introduction/regular expression/text normalization/ edit distance |3 |Lecture |JHC/RWT/SGT |
| | | |Class Discussion | |
|2 |N-gram language models |3 |Lecture |SGT |
| |N-grams | |Class Discussion Hand-on | |
| |Evaluating language models | |practice | |
| |Generalization and zeros | | | |
| |Smoothing | | | |
|3 |Naïve Bayes and sentiment classification |3 |Lecture |JHC |
| |Naïve Bayes (NB) classifiers and training | |Class Discussion | |
| |Optimizing for sentiment analysis | | | |
| |NB as a language model | | | |
|4 |Logistic regression (LR) |3 |Lecture |JHC |
| |Learning in LR and loss function | |Class Discussion Hand-on | |
| |Gradient descent and regularization | |practice | |
| |Multinomial logistic regression | | | |
| |Assignment | | | |
|5 |Vector semantics |3 |Lecture |JHC |
| |Lexical and vector semantics | |Class Discussion Hand-on | |
| |Words and vectors/Word2vec | |practice | |
| |TF-IDF | | | |
| |Assignment | | | |
|6 |Neural networks and neural language models |3 |Lecture |JHC |
| |The XOR problem | |Class Discussion | |
| |Feed forward neural networks | | | |
| |Deep neural networks | | | |
| |Neural language models | | | |
|7 |Part-of-speech tagging |3 |Lecture |SGT |
| |English word classes | |Class Discussion | |
| |Part-of-speech tagging | |Hand-on practice | |
| |Maximum entropy markov models | | | |
| |Assignment | | | |
|8 |Midterm Examination |3 | |JHC |
|9 |Sequence processing with recurrent networks |3 |Lecture |JHC/guest lecturer |
| |Simple recurrent neural network (RNN) | |Class Discussion | |
| |Applications of RNN | | | |
| |Deep networks | | | |
| |LSTM and GRU | | | |
|10 |The representation of sentence meaning |3 |Lecture |JHC/RWT |
| |Event and state representation | |Class Discussion Hand-on | |
| |Computational semantics | |practice | |
| |Assignment | | | |
|11 |Parsing |3 |Lecture |SGT |
| |Syntactic | |Class Discussion | |
| |Statistical | | | |
| |Dependency | | | |
| |Semantic | | | |
|12 |Information extraction |3 |Lecture |JHC/guest lecturer |
| |Name entity recognition | |Class Discussion Hand-on | |
| |Relation extraction | |practice | |
| |Extracting times and events | | | |
| |Assignment | | | |
|13 |Lexicons for sentiment |3 |Lecture |JHC/guest lecturer |
| |Defining emotion | |Class Discussion | |
| |Available sentiment and affect lexicons | | | |
| |Creating affect lexicons by human labeling | | | |
| |Semi-supervised vs supervised | | | |
| |Affect recognition | | | |
|14 |Discourse coherence |3 |Lecture |RWT |
| | | |Class Discussion Hand-on | |
| | | |practice | |
|15 |Machine translation/Q&A/Chatbots |3 |Lecture |JHC/SGT |
| |Model Evaluation | |Class Discussion Hand-on | |
| |Developing models | |practice | |
|16 |Project |3 |Class Discussion |JHC |
| |Presentation and Demonstration | | | |
|17 |Final Examination |3 | |JHC |
|2. Learning Outcome Evaluation Plan |
|No. |Learning Outcomes |Evaluation Activity |Week |Weight |
|1 |2.1.1, 2.1.2, |Midterm examination |8 |30% |
| |2.1.4, 3.1.1, | | | |
| |3.1.3, 3.1.4 | | | |
|2 |2.1.1, 2.1.2, |Final examination |17 |40% |
| |2.1.4, 3.1.1, | | | |
| |3.1.3, 3.1.4 | | | |
|3 |1.1.2, 2.1.3, |Assignments, Project |Throughout semester |30% |
| |3.1.2, 3.1.4, | | | |
| |5.1.1, 5.1.2, | | | |
| |5.1.3 | | | |
| | | | | |
| |4.1.1 | | | |
| | | | | |
| |2.1.1, 2.1.2, | | | |
| |2.1.4, 3.1.1, |Class participation | | |
| |3.1.3, 3.1.4 | | | |
| | | | | |
| | |Quizzes | | |
Section 6 Teaching/learning resources
|1. Textbook |
|Speech and Language Processing, 3rd ed draft, Daniel Jurafsky and James H. Martin, Sept 2018 – available online |
|2. Documents and important information |
|Enrollment in Natural Language Processing MOOC course. |
|3. Suggested readings and other resources |
|Analysing Discourse Topics and Topic Keywords,Watson Todd, R., Semiotica vol. 184 no. 1-4 |
|pp. 251-270. (2011) |
|Discourse Topics, John Benjamins Publishing Co., Nov 2016 - |
|Online resources; R, Python, Java, and/or Octave/Matlab |
Section 7 Course Evaluation and Improvement
|Course evaluation strategy by students |
|Discussion among students and lecturer |
|Investigate from students’ behaviors |
|Teaching and lecturer evaluation form |
|Teaching evaluation strategies |
|Result of examinations |
|Teaching improvement methods |
|Brainstorm will be used during seminar on teaching improvement |
|Verification of student learning outcomes |
|Reevaluate grading process of random students by other lecturers |
|Verify the examination by program committees |
|Course review and improvement plans |
|This course will be reviewed and made adjustment every three years according to evaluation and Learning Development Outcome in Section 4 |
|Reviews from and discussion with professionals in IT industry will improve the quality of course. |
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