COMP SCI 7417 - Applied Natural Language Processing

North Terrace Campus - Semester 1 - 2020

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.

  • General Course Information
    Course Details
    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 Staff

    Course Coordinator: Dr Lingqiao Liu

    Course Timetable

    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
  • Learning Outcomes
    Course Learning Outcomes
    On 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
    3,4
  • Learning Resources
    Required Resources
    No 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 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.

    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 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.
    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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.


    The course includes three assignments will be increased to 30% 30% and 40%. The difficulty level of the second and the third assignments will be increased. The third assignment is open-ended and will like a mini-project. It will be released soon to give you more time (but due at the end of this semester).


    CBOK Legend
    1. Abstraction
    2. Design
    3. Ethics
    4. Interpersonal Communication
    5. Societal Issues
    6. History & Status of the Discipline
    7. Hardware & Software
    8. Data & Information
    9. Programming
    10. Human Computer Interfaces
    11. 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 Requirements
    Hurdle 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 Detail
    Final 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.
    Assignments
    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.
    Submission
    Assignment
    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:

    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
    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.

    Examination
    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.
    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 Access Adelaide.

  • 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.

  • Student Support
  • Policies & Guidelines
  • 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’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.