COMP SCI 7417 - Applied Natural Language Processing
North Terrace Campus - Semester 1 - 2021
General Course Information
Course Code COMP SCI 7417 Course Applied Natural Language Processing Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Assumed Knowledge COMP SCI 7317 Course Description This course examines machine learning techniques that obtain leading results on the problem of natural language processing (NLP). NLP is a critical step towards effective communication between people and machines. You will learn how to represent words and text, the use of deep recurrent models for text prediction, and issues that separate NLP from other application domains. This will be reinforced by applying deep learning tools to NLP through examples and practical projects.
Course Coordinator: Dr Lingqiao Liu
The full timetable of all activities for this course can be accessed from Course Planner.
Date Lecture Topic 3 Mar Introduction to Natural Language Processing 1 10 Mar Regular Expression and Text Preprocessing 2 17 Mar N-gram and Naïve Bayes 3 24 Mar Machine Learning in NLP I: Basic Concept and Shallow Approach 4 31 Mar Machine Learning in NLP II: Deep Learning Approach 5 ----------------------------------------------- Middle Break ------------------------------------ 7 Apr Vector Semantics 6 28 Apr Part of Speech Tagging, Named Entity Recognition and Sequence Labeling in NLP 7 5 May Introduction to Parsing and Context Free Grammar 8 12 May Syntactic Parsing 9 19 May Representing meaning of sentences 10 26 May Other NLP Problems and Applications 11 2 June Frontiers of Natural Language Processing 12
Course Learning OutcomesOn successful completion of this course, students will be able to:
1 Understand the basic concepts and basic algorithms of Natural language processing.
2 Ability to use existing natural language processing tools to conduct basic natural language processing, such as text normalization, named entity extraction, or syntactic parsing.
3 Ability to use machine learning tools to build solutions for natural language processing problems.
4 Ability to 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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3,4 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
3,4 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
4 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
4 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesNo textbook required.
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.
Recommended ResourcesRecommended 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 LearningOur course forum is accessible via the Canvas.
Excellent external courses available online:
1. CS224n: Natural Language Processing with Deep Learning
2. Natural Language Processing
Learning & Teaching Activities
Learning & Teaching ModesThe course will be primarily delivered through three activities:
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.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
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.
Learning Activities SummaryStudents 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.
Specific Course RequirementsKnowing 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.
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.
Three assignments and one online open-book exam will be used for assessment. The marks for each components are 20% for each assignment and 40% for the exam.
- Interpersonal Communication
- Societal Issues
- History & Status of the Discipline
- Hardware & Software
- Data & Information
- Human Computer Interfaces
- Systems Development
Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in https://www.acs.org.au/accreditedcourses-and-jobs.
Assessment Related RequirementsHurdle Requirement: If your overall mark for the course is greater than
44 F but, your mark for the final written exam is less than 40%, your
overall mark for the course will be reduced to 44 F.
Assessment DetailFinal written exam
This will be a 2-hour exam at the end of the course/semester. The exam will assess your knowledge and understanding of the course topics, as well as the abiliity to use the knowledge for problem solving. The exam is open-book. Books, lecture notes, slides print-out, calculators and paper dictionaries (English to foreign language) are permitted. The use of internet is not permitted.
Each student is expected to complete assignments in the form of report and programming work. The assignments must be completed individually and all submissions are to be made under the declaration of adherring to the academic honesty principles. Submissions will be subjected to plagiarism checks. This course has a zero-tolerance policy towards academic honesty violations. Offenders will be duly subjected to university procedures for dealing with academic honesty cases.
Assignment solutions are to be submitted through Canvas.
No physical submissions of work will be accepted unless specifically requested by the lecturer.
Marks will be capped for late submissions, based on the following schedule:retrospectively. Applications for extensions must be made to the course coordinator by e-mail or hard copy and must include supporting documentation – medical certificate or letter from the student counselling service.
1 day late – mark capped at 75%
2 days late – mark capped at 50%
3 days late – mark capped at 25%
more than 3 days late – no marks available.
Extensions to due dates will only be considered under exceptional medical or personal conditions and will not be granted on the last day due, or
The examinations office will schedule the final exam. Students are expected to be available until after the supplementary examination period (precise dates are available from university calendar or exams office). No additional arrangments will be given if students are offered supplementary exams but are unable to attend.
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 Access Adelaide.
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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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- Copyright Compliance Policy
- Coursework Academic Programs Policy
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- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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