CSC 2519 -- Natural Language Semantics

Fall 2010


Index of this document


Contact information

Instructor: Gerald Penn
Office: PT 396B (St. George campus)
Tel: 978-7390
Email: gpenn@cs.utoronto.ca
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Meeting times

Lectures: T 3-5, PT 378
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Presented Readings

Who
When
What
Where
Siavash Kazemian 7 December
Bagpack: A General Framework to Represent Semantic Relations, A. Herdagdelen and M. Baroni
Scaling Distributional Similarity to Large Corpora, J. Gorman and J.R. Curran
GEMS 1, 2009
COLING/ACL-06
N/A 30 November NO MEETING
Jackie Cheung 23 November
Measuring Distributional Similarity in Context, G. Dinu and M. Lapata
Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks, R. Socher, C.D. Manning and A.Y. Ng
EMNLP-10
NIPS 2010
Milad Eftekhar 16 November
Unsupervised Classification with Dependency Based Word Spaces, K. Rothenhaeusler and H. Schuetze
Convolution Kernels for Natural Language, M. Collins and N. Duffy
A Study of Convolution Tree Kernel with Local Alignment, L. Zhang and K-P. Chan
GEMS 1, 2009
NIPS 2001
GEMS 1, 2009
Matthew Skala 16 November
Word Space Models of Lexical Variation, Y. Peirsman and D. Speelman
Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space, E. Sagi, S. Kaufmann and B. Clark
GEMS 1, 2009
GEMS 1, 2009
Paul Cook 9 November
Frames and the Semantics of Understanding, C. J. Fillmore
Background to FrameNet, C.J. Fillmore, C.R. Johnson and M.R.L. Petruck
Manifold Learning for the Semi-Supervised Induction of FrameNet Predicates: an Empirical Investigation, D. Croce and D. Previtali
Quaderni di Semantica 6(2), 1985
Intl. Jounral of Lexicography 16(3):235-250.
GEMS 2, 2010
Tim Fowler 2 November Automatic Labelling of Semantic Roles, D. Gildea and D. Jurafsky Computational Linguistics 28(4).
Serguei Zinine 2 November Semantic Similarity based on Corpus Statistics and Lexical Taxonomy, J. J. Jiang and D. W. Conrath ROCLING 10, 1997.
David Barton 26 October
Introduction to WordNet
Semantic Similarity in a Taxonomy: an Information-based Measure and its Application to Problems of Ambiguity in Natural Language, P. Resnik
Five Papers on WordNet, Princeton University, pp. 1-25 (the first two papers).
JAIR 11.
Michael Tao 19 October
Context-theoretic Semantics for Natural Language: an Overview, D. Clarke
Semantic Composition with Quotient Algebras, D. Clarke, R. Lutz and D. Weir
GEMS 1, 2009
GEMS 2, 2010
Julian Brooke 12 October
Ranking Parapharses in Context, S. Thater, G. Dinu and M. Pinkal
Contextualizing Semantic Representations using Syntactically Enriched Vector Models, S. Thater, H. Fuerstenau and M. Pinkal
ACL/IJCNLP-09 Workshop on Applied Textual Inference
ACL-10
Tong Wang 12 October
A Structured Vector Space Model for Word Meaning in Context, K. Erk and S. Pado'
Exemplar-based Models for Word Meaning in Context, K. Erk and S. Pado'
EMNLP-08
ACL-10
Eric Corlett 5 October
Capturing Nonlinear Structure in Word Spaces through Dimensionality Reduction, D. Jurgens and K. Stevens
Locality Preserving Projections, X. He and P. Niyogi
GEMS 2, 2010
NIPS-16, 2003.
Jackie Cheung 28 September Vector-based models of semantic composition, J. Mitchell and M. Lapata. ACL-08
Varada Kolhatkar 28 September Expectation vectors: a semitotics-inspired approach to geometric lexical-semantic representation, J. Washtell. GEMS 2, 2010
Gerald Penn 14,21 September;
5,19 October
Singular Value Decomposition Foundations of Statistical Natural Language Processing, C. Manning and H. Schuetze, MIT Press, 1999.

Additional Readings for the Lectures

Title Author Publication Details
Logic, Language and Meaning, vols. 1 and 2 L.T.F. Gamut University of Chicago Press, 1991.
Type-Logical Semantics B. Carpenter MIT Press, 1997.

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Tentative course outline

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Calendar of important course-related events

Date Event
Tue, 14 September First meeting
Wed, 6 October Last day to add course
Tue, 19 October Proposals for final projects due
Wed, 3 November Last day to drop course
Tue, 30 November No meeting
Tue, 7 December Last meeting
Fri, 17 December Term papers/projects due

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Evaluation

Your final mark will be determined by a term paper/project, and a presentation of one or more papers in class.  The relative weights of these components towards the final mark are shown in the table below:
Class presentation 20%
Term paper/project 80%

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Announcements

In this space, you will find announcements related to the course. Please check this space at least weekly. Back to the index

Gerald Penn, 25 November, 2010
This web-page was adapted from the web-page for another course, created by Vassos Hadzilacos.