• Class Number 5697
  • Term Code 3360
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Dawei Chen
    • Charini Nanayakkara
    • Prof Graham Williams
  • Class Dates
  • Class Start Date 24/07/2023
  • Class End Date 27/10/2023
  • Census Date 31/08/2023
  • Last Date to Enrol 31/07/2023
SELT Survey Results

Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the \document" and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Differentiate between the basic probabilistic theories of language and document structure, information retrieval, and classification, clustering and document feature engineering.
  2. Identify the basic algorithms and software available for probabilistic theories of language and be proficient at using common libraries for natural language processing to perform basic analysis tasks.
  3. Index a document collection for use in an information retrieval system. Demonstrate advanced knowledge of basic theories and algorithms to determine large scale named-entity matching and standardization of names within a collection.
  4. Perform automated classification using probabilistic theories.

Required Resources

A laptop or desktop with a reliable internet connection is required for accessing the course material on Wattle and for completing the practicals and assignments. Python and

Jupyter Notebook will be used extensively in this course so being able to install freely available software will be necessary. An alternative is to have access to a laptop or desktop

where appropriate software is already installed.

Further details on software used, and instructions, can be found on the Wattle site for the course.

The following textbooks will help you to better understand the course material and broaden your understanding. They are both provided online for free by the authors and are

highly recommended. The first book covers information retrieval in a very approachable way, but it goes into much more depth than we will cover in this course. The second book is

a currently evolving new edition of one of the best Natural Language Processing (NLP) textbooks. This book is up to date with the latest approaches and covers many topics in

much greater depth than we will cover in this course.

Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press. 2008.

Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft). 2023.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • Written comments
  • Verbal comments
  • Feedback to the whole class, to groups, to individuals, focus groups

Feedback will be provided before week 7, details provided in Wattle.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session Summary of Activities Assessment
1 Course IntroductionTypical NLP Tasks Quiz 0 (Ungraded)
2 Boolean RetrievalRanked RetrievalLab 1 Assignment 1 Released
3 Evaluation of Information Retrieval SystemsWeb Search BasicsLab 2 Quiz 1
4 Machine Learning Basics Assignment 2 ReleasedAssignment 1 Due
5 Representation in NLPClusteringLab 3
6 Deep Neural Networks: MLP and RNNLab 4 Assignment 2 Due
7 Seq2Seq and AttentionTransformers Assignment 3 Released
8 Pre-trained Language ModelsLab 5 Quiz 2
9 Language ModellingLab 6
10 Syntactic ParsingSemantics Assignment 3 Due
11 Evaluation in NLPMultilingual and Low Resource NLP Quiz 3
12 Course Review

Tutorial Registration

Register for tutorials/labs via MyTimetable.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Quizzes (10%) 10 % * * 1,2,3,4,5,6
Assignment 1 (10%) 10 % 16/08/2023 29/08/2023 1,2,5
Assignment 2 (10%) 10 % 30/08/2023 26/09/2023 1,2,3
Assignment 3 (10%) 10 % 11/10/2023 24/10/2023 1,2,4
Final Exam (60%) 60 % * * 1,2,3,4,5,6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 10 %
Learning Outcomes: 1,2,3,4,5,6

Quizzes (10%)

Three online Wattle quizzes will be offered. Marks for quizzes 1, 2 and 3 will be scaled to contribute 3%, 4% and 3% to the overall course mark, respectively.

Only one attempt is permitted for each quiz. Automated feedback on correct answers is given once the due date is passed and all (pre-arranged) late submissions are

collected. Submission deadlines provided in Wattle.

No late submissions without a pre-arranged extension.

Assessment Task 2

Value: 10 %
Due Date: 16/08/2023
Return of Assessment: 29/08/2023
Learning Outcomes: 1,2,5

Assignment 1 (10%)

A programming assignment that also requires you to provide written answers to questions. Covers the Information Retrieval material in the course. Details provided

through Wattle. Submission is through Wattle. Submission deadline provided in Wattle.

No late submissions without a pre-arranged extension.

Assessment Task 3

Value: 10 %
Due Date: 30/08/2023
Return of Assessment: 26/09/2023
Learning Outcomes: 1,2,3

Assignment 2 (10%)

A programming assignment that also requires you to provide written answers to questions. Covers the Machine Learning material in the course. Details provided through

Wattle. Submission is through Wattle. Submission deadline provided in Wattle.

No late submissions without a pre-arranged extension.

Assessment Task 4

Value: 10 %
Due Date: 11/10/2023
Return of Assessment: 24/10/2023
Learning Outcomes: 1,2,4

Assignment 3 (10%)

A programming assignment that also requires you to provide written answers to questions. Covers the Machine Learning and Natural Language Processing material in

the course. Details provided through Wattle. Submission is through Wattle. Submission deadline provided in Wattle.

No late submissions without a pre-arranged extension.

Assessment Task 5

Value: 60 %
Learning Outcomes: 1,2,3,4,5,6

Final Exam (60%)

The Final exam will be a closed-book exam. Detailed information will be provided via the Wattle course site.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

Online submission is through Wattle. Submissions of code will be run through Moss to assess submissions as an approach to managing Academic Integrity.

Hardcopy Submission

No hardcopy submissions accepted.

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Feedback for assignments will be provided through Wattle.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

No resubmission of assignments.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
Dr Dawei Chen

Research Interests

Machine Learning and Natural Language Processing

Dr Dawei Chen

By Appointment
Charini Nanayakkara

Research Interests

Machine Learning and Natural Language Processing

Charini Nanayakkara

Prof Graham Williams

Research Interests

Prof Graham Williams


Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions