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In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3, pp. Simple coreference resolution with rich syntactic and semantic features. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. In Proceedings of the 4th International Workshop on Semantic Evaluations, pp. Bender, Jonathon Read, Stephan Oepen and Rebecca Dridan. Simple Negation Scope Resolution through Deep Parsing: A Semantic Solution to a Semantic Problem. [data/software] Tanaka, Takaaki, Francis Bond, Timothy Baldwin, Sanae Fujita, and Chikara Hashimoto. Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information. Using syntax to disambiguate explicit discourse connectives in text. Tratz, Stephen, Antonio Sanfilippo, Michelle Gregory, Alan Chappell, Christian Posse, and Paul Whitney. PNNL: a supervised maximum entropy approach to word sense disambiguation. Oepen, Stephan, Erik Velldal, Jan Tore Lønning, Paul Meurer, Victoria Rosén, and Dan Flickinger. Towards hybrid quality-oriented machine translation. [pdf available from course Common View] Packard, Woodley, Emily M. Zhang, Y., Oepen, S., Dridan, R., Flickinger, D., and Krieger, H. Robust Parsing, Meaning Composition, and Evaluation. Integrating Grammar Approximation, Default Unification, and Elementary Semantic Dependencies (unpublished manuscript). In Proceedings of the 23rd International Conference on Computational Linguistics, pp. In Proceedings of the Second Joint Conference on Lexical and Computational Semantics, volume 1, pages 53–58. Probabilistic tree-edit models with structured latent variables for textual entailment and question answering. Aliaksei Severyn, Massimo Nicosia, and Alessandro Moschitti. ikernels-core: Tree kernel learning for textual similarity. In Proceedings of the 23rd International Conference on Computational Linguistics (pp.

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It encompasses analyses of a wide range of phenomena in English, and a key piece of each analysis is the design of the resulting semantic representation.

The MRS representations are built compositionally by the grammar and represent a significant abstraction away from the surface string.

The goal of this seminar is to explore how the MRS representations can be used to inform semantically-sensitive NLP tasks, such as anaphora resolution, event detection, or relation extraction.

We will begin with an overview of MRS, and then move on to an exploration of candidate tasks and how to create machine learning features from MRSs to augment existing solutions to those tasks.

Term projects (which may be done in pairs) will involve selecting an existing annotated data set for a semantically-sensitive task as well as an existing baseline solution and then attempting to improve on the baseline by adding MRS-based features. Syntactically-informed semantic category recognizer for discharge summaries.

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