Latest Module Specifications
Current Academic Year 2025 - 2026
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Description A central goal of Natural Language Processing (NLP) is to develop automated systems capable of understanding and generating natural language, with wide-ranging applications including machine translation, dialogue systems, information extraction, opinion mining and grammar checking. This module will provide students with a theoretical and practical grounding in the core areas, tasks and methods of NLP. Students will be introduced to the challenges of developing modern, data-driven NLP systems against a background of the history of NLP and its rapidly evolving technologies. Through lectures, assignments and a group project, students will learn the necessary background and skills to design, implement, evaluate and understand their own NLP models, using commonly used off-the-shelf toolkits and programming libraries. Topics covered in depth include different NLP tasks such as syntactic and semantic analysis, language generation, and text classification, as well as methods for evaluating NLP systems using automated metrics and human assessment. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Demonstrate awareness of the history of NLP and the emergence in the field over time of increasingly varied and complex tasks and methods 2. Apply in practice knowledge of a range of sequence labeling tasks and available methods and tools to solve them 3. Apply in practice knowledge of word vectors, their typical uses and methods for creating them 4. Apply in practice knowledge of a range of text classification tasks and methods for solving them. 5. Demonstrate an understanding of the main machine learning algorithms used in NLP, including Naive Bayes, logistic regression, support vector machines, hidden Markov models, conditional random fields, and simple neural networks. 6. Design systems to perform core NLP tasks (document classification, tagging, parsing, information extraction, generation) and test alternative solutions on datasets used by the NLP research community 7. Critically review, select and apply appropriate evaluation methods for a range of different NLP tasks. 8. Demonstrate an understanding of the many challenges that remain in the field of NLP, including in relation to evaluation, technical capability, ethics and wider societal responsibilities. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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
Introductory Concepts and History of NLP In the first part of the course, students will be introduced to the field of NLP via a survey of subfields, linguistic concepts, and tasks prevalent in NLP. The field’s history will be traced through the increasingly varied and complex tasks and methods that it has addressed since its beginnings in the middle of the 20th century. This part will introduce the four clusters of NLP problems and methods developed to solve them which are explored in more detail in the next five parts of the module. Syntactic Analysis and Structured Prediction The sequence labelling tasks of part-of-speech tagging, chunking and named entity recognition will be introduced. Hidden markov models and conditional random fields will be described and compared. Different features used in sequence labelling tasks will be discussed. The problem of dependency parsing will be described. The two main approaches of dependency parsing will be covered, namely, transition and graph-based dependency parsing. Distributional Semantics and Word Vectors Students will be introduced to count-based word vectors to represent word meaning, and different methods for creating them will be explored. Students will learn about the range of applications word vectors are used for, in particular their role in semantic reasoning tasks (e.g. word sense disambiguation, paraphrase detection). Word vectors will be situated in the context of related vector-based representations of words and text. Natural Language Generation Common tasks, approaches and methods in NLG will be introduced. The core tasks of natural language generation from structured data, meaning representations and syntactic representations will be explored, and template-based and statistical approaches to the problem will be studied. Document/Text Classification Students will be introduced to NLP applications which involve classifying documents into discrete classes. These include sentiment analysis, language identification and topic classification. A range of text classification methods will be studied in practical contexts. Evaluation The evaluation methods introduced in earlier parts of the module will be placed in a wider context by exploring the purposes of evaluation in NLP, and surveying prevalent evaluation methods. Intrinsic vs. extrinsic, absolute vs. relative, and objective vs. subjective modes of evaluation will be distinguished. Basic concepts in human evaluation of NLP systems will be introduced (covered in more depth in CA6012 Human Factors in NLP). Automated evaluation metrics will be critically reviewed and explored in practical exercises. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
Articles:
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Other Resources
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| To added to MCM Semester 1 NLP Major structures asap. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||