Registry
Module Specifications
Archived Version 2023 - 2024
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Description This module provides students with a practical and theoretical grounding in the following core topics in modern Natural Language Processing: language modelling, language analysis, information extraction and language understanding. Students will learn how these problems are tackled using supervised and semi-supervised machine learning, and they will gain hands-on experience developing machine-learning solutions during the laboratory sessions. Popular benchmark datasets will be employed, including ‘noisy’ datasets containing text from sources such as Twitter and reddit. | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Describe the applications of Natural Language Processing (NLP) in Data Science 2. Illustrate how neural word embeddings underpin modern NLP systems 3. Develop an English language model 4. Evaluate an English language model 5. Develop an English part-of-speech tagger 6. Evaluate an English part-of-speech tagger 7. Develop a sentiment analysis system for English 8. Evaluate a sentiment analysis system for English 9. Develop an English question answer/reading comprehension system 10. Evaluate an English question answer/reading comprehension system 11. Explain the unsolved problems in NLP research 12. Summarize the ethical issues surrounding modern data-driven NLP | |||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Language ModellingNext word prediction using n-gram language models, modern word embeddings including word2vec (Mikolov et al. 2013) and contextualised word embeddings such as BERT (Devlin et al. 2019). Generating text using language modelsLanguage analysis and data extractionPart-of-speech tagging, dependency parsing, named-entity recognition, semantic parsing. Sequence labelling and structured prediction using recurrent neural nets (LSTMs) and transformer networksLanguage understandingSentiment analysis, automatic reading comprehension and question answering. Using information retrieval techniques in question answering. Using language analysis and extraction tools (see above) to improve baseline models for language understanding applications | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||
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