Formal Semantics/Pragmatics

On the formalization of invariant mappings for metaphor interpretation

by Rodrigo Agerri

R. Agerri, J.A. Barnden, M.G. Lee, A.M. Wallington (2007). On the formalization of Invariant Mappings for Metaphor Interpretation. In the Companion Proceedings of the Association of Computational Linguistics Conference (ACL-07), Prague, June 2007, págs. 100-103, ISBN: 978-1-932432-88-6.

Metaphor, inference and domain independent mappings

by Rodrigo Agerri

R. Agerri, J.A. Barnden, M.G. Lee and A.M. Wallington (2007). Metaphor, Inference and Domain Independent Mappings. In G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov, and N. Nikolov (eds.), Proceedings of the Research Advances in Natural Language Processing Conference (RANLP-07), Borovets, Bulgaria, 27-29 September 2007, ISBN: 978-954-91743-7-3.

A note on the aorist

by Hartmut Haberland

In: Jacob L. Mey, ed. Language and discourse: test and protest. A Festschrift for Petr Sgall. Amsterdam: Benjamins. 173-184

The Penn Lambda Calculator: Pedagogical Software for Natural Language Semantics

by Lucas Champollion

Champollion, Lucas, Joshua Tauberer, and Maribel Romero (2007). In T. Holloway King and E. M. Bender (eds.), Proceedings of the Grammar Engineering across Frameworks (GEAF) 2007 Workshop. Stanford, CA, July 13-15 2007. CSLI On-line Publications. See also the homepage of the software application described in this paper: http://www.ling.upenn.edu/lambda

This paper describes a novel pedagogical software program that can be seen as an online companion to one of the... more This paper describes a novel pedagogical software program that can be seen as an online companion to one of the standard textbooks of formal natural language semantics, Heim and Kratzer (1998). The Penn Lambda Calculator is a multifunctional application designed for use in standard graduate and undergraduate introductions to formal semantics: Teachers can use the application to demonstrate complex semantic derivations in the classroom and modify them interactively, and students can use it to work on problem sets provided by the teacher. The program supports demonstrations and exercises in two main areas: (1) performing beta reduction in the simply typed lambda calculus; (2) application of the bottom-up algorithm for computing the compositional semantics of natural language syntax trees. The program is able to represent the full range of phenomena covered in the Heim and Kratzer textbook by function application, predicate modification, and lambda abstraction. This includes phenomena such as intersective adjectives, relative clauses and quantifier raising. In the student use case, emphasis has been placed on providing "live" feedback for incorrect answers. Heuristics are used to detect the most frequent student errors and to return specific, interactive suggestions.

Move and accommodate: A solution to Haddock's puzzle

by Lucas Champollion

Champollion, Lucas, and Uli Sauerland (2010). Move and accommodate: A solution to Haddock's puzzle. Empirical Issues in Syntax and Semantics 8, Olivier Bonami and Patricia Cabredo Hofherr (eds.) 2010.

This paper proposes a solution to Haddock's puzzle: Why do embedded definites ("the rabbit in the hat") have... more This paper proposes a solution to Haddock's puzzle: Why do embedded definites ("the rabbit in the hat") have different presuppositions from what would be expected on a strictly compositional semantics? For example, "the rabbit in the hat" does not presuppose that there is a unique hat, but only that there is a unique combination of a hat and a rabbit in it. This is unexpected if the presupposition of "the hat" is computed locally. The solution is given in terms of the independently attested mechanisms of intermediate presupposition accommodation and quantifier raising: the inner definite is raised above the outer one, whose uniqueness presupposition is then accommodated into the inner definite. Since quantifier raising is subject to island constraints, the account predicts that embedded definites should be sensitive to islands. This prediction is confirmed in an experiment with 800 participants conducted with Amazon Mechanical Turk.