Goh, J., Pauly, A., & Valenti, M. (2024). The Weakness of Finding Descending Sequences in Ill-Founded Linear Orders. In Twenty Years of Theoretical and Practical Synergies (pp. 339-350). Springer Nature Switzerland.
Valenti, M., GOH, J., Pauly, A., & VALENTI, M. (2021). FINDING DESCENDING SEQUENCES THROUGH ILL-FOUNDED LINEAR ORDERS. The Journal of Symbolic Logic, 86(2), 817-854.
Lempp, S., Miller, J., Pauly, A., Soskova, M., & Valenti, M. (2024). Minimal covers in the Weihrauch degrees. Proceedings of the American Mathematical Society, 152(11), 4893-4901.
Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Computably Discrete Represented Spaces. In Lecture Notes in Computer Science (pp. 349-364). Springer Nature Switzerland.
Franklin, J., Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Represented Spaces of Represented Spaces. In Lecture Notes in Computer Science (pp. 47-61). Springer Nature Switzerland.
Lempp, S., Miller, J., Pauly, A., Soskova, M., & Valenti, M. (2024). Minimal covers in the Weihrauch degrees. Proceedings of the American Mathematical Society, 152(11), 4893-4901.
Valenti, M., GOH, J., Pauly, A., & VALENTI, M. (2021). FINDING DESCENDING SEQUENCES THROUGH ILL-FOUNDED LINEAR ORDERS. The Journal of Symbolic Logic, 86(2), 817-854.
Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Computably Discrete Represented Spaces. In Lecture Notes in Computer Science (pp. 349-364). Springer Nature Switzerland.
Franklin, J., Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Represented Spaces of Represented Spaces. In Lecture Notes in Computer Science (pp. 47-61). Springer Nature Switzerland.
Goh, J., Pauly, A., & Valenti, M. (2024). The Weakness of Finding Descending Sequences in Ill-Founded Linear Orders. In Twenty Years of Theoretical and Practical Synergies (pp. 339-350). Springer Nature Switzerland.
This module introduces the notion of grammars for defining the syntax of
formal languages, especially programming languages. It introduces
the limits of computation using Turing Machines and other models of
computation.
CSCM22
Symbolic Artificial Intelligence and Natural Language Processing
This module will introduce the symbolic, statistical, and structural properties of language, explain and compare some of the major approaches in symbolic AI and Natural Language Processing, and explain their relevance to practical computing application areas.
CSCM422
Symbolic AI and Natural Language Processing
This module provides a comprehensive introduction to the foundational concepts of symbolic logic and the Chomsky hierarchy, covering regular languages, grammars, and regular expressions. It explores the limitations of regular languages and delves into context-free grammars, alongside practical implementation and evaluation of grammars. The course transitions towards probabilistic natural language processing (NLP) with topics such as text classification, N-gram language models, and sequence models. Additionally, it examines distributional semantics and concludes with an overview of deep learning approaches in NLP.