COMP SCI 7417 - Applied Natural Language Processing
North Terrace Campus - Semester 1 - 2024
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General Course Information
Course Details
Course Code COMP SCI 7417 Course Applied Natural Language Processing Coordinating Unit Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Prerequisites (COMP SCI 7210 and COMP SCI 7211) or COMP SCI 7201; M. Computer Science students are exempted from this pre-requisite. Assumed Knowledge MATH 7027 and (COMP SCI 7317 or COMP SCI 7327 or MATH 7107) Assessment Individual assignments, quizzes and mini projects Course Staff
Course Coordinator: Dr Alfred Krzywicki
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to: 1 Define basic concepts and explain basic algorithms of Natural language processing 2 Use existing natural language processing tools to conduct basic natural language processing, such as text normalization, named entity extraction, or syntactic parsing 3 Use machine learning tools to build solutions for natural language processing problems 4 Decompose a real-world problem into subproblems in natural language processing and identify potential solutions University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1,2,3,4 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1,2,3,4 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
3,4 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
4 Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
4 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
2,3 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
1,2,3,4 -
Learning Resources
Required Resources
All required resources for this course will be provided online via the MyUni platform.Recommended Resources
Recommended books:
1. Speech and Natural Language Processing by Dan Jurafsky and James H. Martin
2. Applied Natural Language Processing with Python by Beysolow II, Taweh
Some parts of the lecture slides are based on book 1.Online Learning
Our course forum is accessible via the Canvas.
Excellent external courses available online:
1. CS224n: Natural Language Processing with Deep Learning
http://web.stanford.edu/class/cs224n/
2. Natural Language Processing
https://www.youtube.com/watch?v=3Dt_yh1mf_U&list=PLQiyVNMpDLKnZYBTUOlSI9mi9wAErFtFm -
Learning & Teaching Activities
Learning & Teaching Modes
The course will be primarily delivered through three activities:
- Lectures
- Workshops
- Assignments
Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and two-way communication are also expected during lectures to enrich the learning experience. The workshops will discuss how to apply the knowledge from lectures to practical problems. The assignments will reinforce concepts by their application to problem solving. This will be done via programming work. All material covered in the lectures and assignments are assessable.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Students are expected to spend 9-10 hours per week on this course.
There will be 1-2 hours contact time for learning and teaching activities and students will be working individually and in groups 8-9 hours to carry out the required learning and teaching activities for acquiring the expected knowledge, understanding and skills in this course.Learning Activities Summary
Students are encouraged to attend lectures as material presented in lectures often includes more than is on the slides. Students are also encouraged to ask questions during the lectures. Slides will be available via the subject web page.
This is a 3-unit course. Students are expected to spend about 8 hours per week on the course. This includes a 2-hour lecture, 2-hour self study and up to 4 hours per week on completing assignments.
Assigmment work will be subjected to deadlines. Students are expected to manage their time effectively to allow timely submission, especially with consideration to workload of other courses.
Specific Course Requirements
Knowing some basic statistics, probability, linear algebra and optimisation would be helpful, but not essential. They will be covered when needed.
Ability to program in Python is required. -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Two assignments (15% and 30% of total grade) involving design and coding practice for solving NLP problems.
10 weekly mini-quizes (10% of the grade in total) focused on the theoretical part of the course. Each mini-quiz will take one hour or less to complete.
Final test (40% of the grade) focused on the theoretical part of the course.
Some mark (up to 5%) for active participation may also be awarded.
Assessment Related Requirements
Python coding practice, knowledge of course material and basic Machine Learning skills are required to complete assessments.Assessment Detail
Weekly quizzes in weeks 1-10
Due: each week 1-10
Percentage of grade: 10%
Type: Quiz, individual
Assignment 1: Text classification and a sentiment analysis system
Due: Week 4
Percentage of grade: 15%
Type: Open-book, individual
Assessment Documents: Code and report
Assignment 2: NLP application project: building an information retrieval, question answering and a dialog system
Due: Week 11 (tentative)
Percentage of grade: 30%
Type: Open-book, individual or group
Assessment Documents: Code and Report
Final Test: NLP Algorithms and Applications
Due: Week 13 (tentative)
Percentage of grade: 40%
Type: Open-book quiz, in-class, individual
Remaining up to 5% of grade will be awarded for active participation in workshops.Submission
Submission details for all activities are available in MyUni but the majority of your submissions will be online and may be subjected to originality testing through Turnitin or other mechanisms. You will receive clear and timely notice of all submission details in advance of the submission date.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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