MASTER IN LINGUISTICS – COMPUTATIONAL LINGUISTICS

Natural Language Processing (NLP) has transformed numerous sectors by enabling advanced generative AI technologies, including large language models (LLMs), and fostering innovations such as real-time translation, intelligent virtual assistants, and automated content generation. The Master’s programme in Computational Linguistics at UPCité equips students with both theoretical understanding and hands-on expertise in NLP, preparing them for careers in the industry or academia through an interdisciplinary curriculum encompassing computer science, linguistics, and artificial intelligence.

The programme is organised around three core types of lectures:
1. NLP Lectures that provide a comprehensive introduction to the tools, algorithms, and systems that underpin contemporary NLP applications. A strong emphasis is placed on both theoretical principles and practical implementation, including dedicated modules on the architecture, training, and deployment of LLMs. Students gain insight into how these models work, their capabilities and limitations, and their societal implications.
2. Linguistics Lectures aimed at ensuring that students develop a robust understanding of the theoretical frameworks necessary to address real-world language data, whether textual or spoken. This linguistic grounding is essential both for understanding the hypotheses underlying the idea that LLMs and other NLP systems can effectively process natural language, and for critically engaging with their outputs.
3. Computer Science and Data Science Lectures that equip students with the computational and analytical skills required to design, build, and evaluate NLP systems. Topics include machine learning (including deep learning), software engineering, and data processing.

Graduates of the programme will gain both theoretical grounding and practical skills in:

• Analysing and processing large, varied text corpora using methods from data science and AI
• Understanding and applying advanced NLP, machine learning, and AI algorithms, including the theory and use of LLMs
• Developing and fine-tuning deep learning models with modern frameworks (e.g., PyTorch, TensorFlow)
• Designing original NLP algorithms and systems, informed by theoretical and empirical approaches
• Applying modern linguistic theories to the analysis and modelling of diverse languages
• Addressing the ethical and societal challenges of AI and LLMs, including bias, fairness, transparency, and environmental impact