Course pages 2011–12
Syntax and Semantics of Natural Language
Principal lecturers: Prof Ted Briscoe, Dr Stephen Clark
Taken by: MPhil ACS, Part III
Prerequisites: Introduction to Natural Language Processing L100 core module or equivalent background
This module provides an introduction to the formal syntax and semantics of natural language, in particular Montague-style compositional semantics using a Categorial Grammar model of syntax. Half of the module will focus on the theory of syntax, followed by an example of how recent advances in parsing technology allow such a theory to be implemented in practice, operating on naturally occurring text. The other half of the module focuses on truth-conditional compositional semantics of sentences, computational implementation of this approach, and probabilistic inference for semantic interpretation.
- Introduction to (Combinatory) Categorial Grammar (CCG). [1 lecture]
- English syntax in the CCG framework. [3 lectures]
- Introduction to statistical parsing. [1 lecture]
- Constructing a wide-coverage CCG English grammar. [1 lecture]
- Wide-coverage robust statistical parsing with CCG. [2 lectures]
- Introduction to natural language semantics. [1 lecture]
- Montague semantics; compositional semantics, typed lambda calculus, quantification, intensionality. [3 lectures]
- Robust, wide-coverage implementation and underspecification. [2 lectures]
- Probabilistic inference and semantic interpretation. [2 lectures]
All lectures will be given by Professor Briscoe or Dr Clark.
On completion of this module, students should:
- understand how the syntax of natural language sentences can be modelled using a type-driven (Combinatory) Categorial Grammar;
- understand how a wide-coverage grammar of English can be constructed;
- have studied one approach to statistical parsing in detail;
- understand how the meaning of natural language sentences can be modelled using a logical, model-theoretic approach;
- understand how the meaning of natural language sentences can be constructed using Frege's principle of compositionality;
- understand how this approach to meaning can be combined with probabilistic inference;
- gain an appreciation of how syntactic and semantic theory can be implemented in practice.
- Four ticked take-home tests or short practicals. Each ticked test is worth 5% of the final assessment for the course. Tests will be due in one week after assignment and ticked (with feedback) by Professor Briscoe and Dr Clark.
- One final take-home exam covering all the material taken at beginning of Easter Term. Final take-home exam will contribute 80% to the final assessment. Questions set and marked by Professor Briscoe and Dr Clark.
Steedman, M. (with Baldridge, J.) (forthcoming). Combinatory
categorial grammar. To appear in Non-transformational syntax
(eds. Borsley, R. & Borjars, K.). Available
Clark, S. & Curran, J.R. (2007). Wide-coverage efficient statistical parsing with CCG and log-linear models. In Computational linguistics, 33(4), pp.493-552. Available here.
Cann, R. (1993). Formal semantics. Cambridge University Press. (Google Books, UL, etc.)
Bos, J. & Blackburn, P. (2004). Working with discourse representation theory. Available here.
Bos, J., Clark, S., Curran, J.R. & Hockenmaier, J. (2004). Wide-coverage semantic representations from a CCG parser. In Proceedings of COLING-04, pp.1240-1246, Geneva, Switzerland. Available here.
Bos, J. (2008). Wide-coverage semantic analysis with Boxer. 2nd Conference on semantics in text processing. Available here.
Garrette, D., Erk, K. & Mooney, R. (2011). Integrating logical representations with probabilistic information using Markov logic. International workshop on computational semantics. Available here.