University of Technology Sydney

41043 Natural Language Processing

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:

Undergraduate

Result type: Grade and marks

Requisite(s): 41040 Introduction to Artificial Intelligence OR 31250 Introduction to Data Analytics

Description

Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English). It includes a number of AI areas, such as text understanding and summarization, machine translation, and sentiment analysis. This subject introduces the foundations of technologies in NLP and their applications to practical problems. It brings together the state-of-the-art research and practical techniques in NLP, providing students with the knowledge and capacity to conduct NLP research and to develop NLP projects.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Explain the advantages and disadvantages of different NLP technologies and their applicability in different business situations. (D.1)
2. Identify NLP applications in business and social contexts. (B.1)
3. Reflect on their own skill development. (F.1)
4. Organise and implement an NLP project in a business environment. (C.1)
5. Interpret the results of an NLP project. (E.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Socially Responsible: FEIT graduates identify, engage, interpret and analyse stakeholder needs and cultural perspectives, establish priorities and goals, and identify constraints, uncertainties and risks (social, ethical, cultural, legislative, environmental, economics etc.) to define the system requirements. (B.1)
  • Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
  • Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
  • Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
  • Reflective: FEIT graduates critically self-review their performance to improve themselves, their teams, and the broader community and society. (F.1)

Contribution to the development of graduate attributes

Engineers Australia Stage 1 Competencies

This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:

  • 1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
  • 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
  • 1.5. Knowledge of engineering design practice and contextual factors impacting the engineering discipline.
  • 2.1. Application of established engineering methods to complex engineering problem solving.
  • 2.2. Fluent application of engineering techniques, tools and resources.
  • 3.2. Effective oral and written communication in professional and lay domains.
  • 3.5. Orderly management of self, and professional conduct.

Teaching and learning strategies

This subject includes combined workshop and laboratory sessions (2 hours) and research and development work for the assignments. The workshop/laboratory sessions will have both individual and group activities, covering NLP theories and algorithms, hands-on exercise on NLP tools, and the understanding and interpretation of NLP project results.

Students are expected to read the program section of the subject outline for instructions regarding required pre-reading and preparation to be undertaken before each class. Pre-reading materials and activities for workshops will be made available on Canvas. During the workshop activities, students will receive various types of feedback in a collaborative and discursive learning context, from their fellow peers and lecturers/tutors. In the assessments, students will need to collaborate to propose research ideas and form groups to develop a research project.

Content (topics)

  1. Text processing and normalization
  2. Part-of-speech tagging
  3. Language and topic modelling
  4. Sentiment Analysis
  5. Machine Translation
  6. Deep learning models for NLP

Assessment

Assessment task 1: Industry and Society Understanding and Analysis

Intent:

To understand the current industry and society needs with respect to NLP-related jobs and skills.
Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

2 and 3

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1 and F.1

Type: Report
Groupwork: Individual
Weight: 20%
Length:

A report up to 1000 words, including references.

Assessment task 2: Quiz

Intent:

To undertake a quiz.
Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

D.1

Type: Quiz/test
Groupwork: Individual
Weight: 30%

Assessment task 3: Project Development

Intent:

To develop a project report and a presentation.
Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

4 and 5

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and E.1

Type: Report
Groupwork: Group, individually assessed
Weight: 50%
Length:

A report up to 3500 words, including references, plus an oral defence.

Minimum requirements

To pass this subject, students must achieve an overall mark of 50% or greater.

Required texts

Jurafsky, David, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Upper Saddle River, NJ: Prentice-Hall, 2000. ISBN: 0130950696.

Manning, Christopher D., and Hinrich Schütze. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999. ISBN: 0262133601.

Recommended texts

NLP tutorial: https://www.upf.edu/web/mtg/nlp-tutorial

Foundations of Statistical Natural Language Processing: https://nlp.stanford.edu/fsnlp

The structure of modern English: https://muse.jhu.edu/article/19425