Course Details
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Brief Description
This course qualifies for 80% funding under the HCI Micro-Credential Course Learner Subsidy. Check fees section for details and eligibility. Please Note: Applicants may only apply for and receive, one subsidised course per semester.
This MicroCred introduces learners to the world of Natural Language Processing (NLP). This module covers the fundamentals of statistical NLP and its techniques and applications with a foundational approach. NLP is a set of ICT skills and techniques that allows human language and text to be understood by electronic devices and computer systems.
NLP allows the device to understand human interactions. NLP focuses on translating human speech, gestures, and text into actionable data for the system. NLP works in the background to enable virtual assistants, chatbots, grammar and sentiment checkers, and webpage translation. Combined with machine learning algorithms, NLP creates systems that can be trained to perform tasks and get better through experience. Drawing from computer science and computational linguistics, among other disciplines, NLP attempts to fill the gap between human communication and computer understanding.
Please ensure you enter the Module Code above when applying for this module. Applications without this cannot be processed. You may apply for more than one module under the same application.
|
Module Description |
Module Code |
NFQ Level |
ECTS Credits |
Start Date |
Cost |
|
NLP An Introduction |
MN5001 |
9 |
6 |
Sept. 2025 |
€1,250 |
This micro-credential represents a single module within a larger further award (eg. Certificate, Diploma, Masters). By taking this micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award. The programmes associated with this MicroCred are:
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 Naïve 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).
Learning Outcomes
- On successful completion of this module, students will be able to:
- Use regular expressions to match complex patterns.
- Implement various text pre-processing steps (tokenization, normalization, stemming, lemmatization, and sentence segmentation).
- Use minimum edit distance and phonetic matching to measure the similarity between two strings.
- Learn an N-gram language model from a corpus and deploy the model to generate text.
- Implement a spelling correction program.
- Implement a naïve bayes text classification system and evaluate its performance using a standard benchmark dataset.
- Implement a sentiment analysis system and evaluate its performance using a standard dataset.
- Appreciate using third-party state-of-the-art NLP libraries such as Spacy, NLTK, TextBlob, and CoreNLP.
- Appreciate the role of third-party NLP cloud platforms, such as IBM Watson's Natural Language Understanding, Google Cloud Natural Language, Amazon Comprehend, and Microsoft Azure Text Analytics API, in advancing NLP applications.
Assessment:
There is no terminal exam. You will be assessed through continuous skill-based assignments.
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.
Entry requirements are established to ensure the learner can engage with the course material and assessments, at a level suitable to their needs, and the academic requirements of the module. By applying to this micro-credential, you are confirming that you have reviewed and understand any such requirements, and that you meet the eligibility criteria for admission.
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.
€1,250
HCI Micro-Credential Course Learner Subsidy - Candidates who satisfy the eligibility criteria can qualify for 80% funding subject to the availability of places. To clarify eligibility please go to Eligibility Criteria