Topics covered include:

Basic Text Processing: Regular Expressions, Word Tokenization, Normalization, Stemming and Lemmatization, and Sentence Segmentation.
String Similarity: Minimum Edit Distance, Backtrace and Alignment, Weighted Minimum Edit Distance, Phonetic Matching, Real-world applications (record de-duplication). N-gram Language Models: Introduction to N-grams, Estimating N-gram Probabilities, N-grams Evaluation and Perplexity, Generalization and Zeros, Add-One (Laplace) Smoothing, (Interpolation, Good Turing Smoothing, Kneser Ney Smoothing), Google Books N-gram Corpus, Zipf's law. Spelling Correction: Introduction to the task of Spelling Correction, The Noisy Channel Model of Spelling, Real Word Spelling Correction, Peter Norvig's Spell Checker, State of the Art Systems. Text Classification: Introduction to the task of text classification, Introduction to Naive Bayes, Formalizing the Naive Bayes Classifier, Naive Bayes Learning, Naive Bayes Relationship to Language Modeling, Precision, Recall, and the F measure, Text Classification Evaluation (micro; macro averaging), Practical Issues in Text Classification, Manual labelling tools and techniques (Amazon Mechanical Turk, brat, doccano, inception). Sentiment Analysis: Introduction to the task of Sentiment Analysis, the baseline algorithm for Sentiment Analysis (tokenization, feature extraction, classification), Sentiment Lexicons, Learning Sentiment Lexicons, and Other Sentiment Tasks (aspects, attributes, targets).

The principal entry requirement is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline.

Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.