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This course introduces central problems and methods in natural language processing. Through their experiences in this course, students will be able to apply and evaluate standard methods to new sets of language data. The course will enable students to design an application of natural language processing for a NZ-specific context and evaluate the performance of that application against reasonable baselines.
2022 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.In this course we will examine Natural Language Processing theory and applications with an emphasis on how NLP algorithms are built typically, though not exclusively, using statistical machine learning. The theoretical topics we will cover include:• Encoding natural language as features.• Estimating features using smoothing, normalization, sampling, and expectation-maximization.• Classifying text, training and cross-validation.• Distributed word representations such as skip-grams, word2vec and evaluating stability and similarity.• Language models: training and evaluation (perplexity), word prediction, and other applications.• Sequence models: problem of transitions, Viterbi algorithm, and parsing Applications of these concepts that we will look at include:• Corpus similarity measures• Building dictionaries• Named-entity recognition• Part-of-speech tagging• Language identification• Topic classification• Finding lexical clusters• Phrase completion• Predicting sentence probabilities
(1) COSC262; (2) Approval by the Head of Department of Computer Science and Software Engineering
Please note that the course activity times advertised here are currently in draft form, to be finalised on Monday 31 January 2022 for S1 and whole year courses, and Monday 27 June 2022 for S2 courses. Please do hold off enquiries about these times till those finalisation dates.
2022 Covid-19 Update: Please refer to the course page on AKO | Learn for all information about your course, including lectures, labs, tutorials and assessments.
The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:1. You must achieve an average grade of at least 50% over all assessment items.2. You must achieve an average mark of at least 45% on invigilated assessment items.If you satisfy both these criteria, your grade will be determined by the following University-wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a C+ grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment. Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required. Special consideration is not available for items worth less than 10% of the course.Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.
Domestic fee $1,051.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Computer Science and Software Engineering