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|Title:||A generative model for parsing natural language to meaning representations||Authors:||Lu, W.
|Issue Date:||2008||Citation:||Lu, W.,Ng, H.T.,Lee, W.S.,Zettlemoyer, L.S. (2008). A generative model for parsing natural language to meaning representations. EMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL : 783-792. ScholarBank@NUS Repository.||Abstract:||In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient training and decoding. In experiments, we demonstrate that the model, when coupled with a discriminative reranking technique, achieves state-of-the-art performance when tested on two publicly available corpora. The generative model degrades robustly when presented with instances that are different from those seen in training. This allows a notable improvement in recall compared to previous models. © 2008 Association for Computational Linguistics.||Source Title:||EMNLP 2008 - 2008 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference: A Meeting of SIGDAT, a Special Interest Group of the ACL||URI:||http://scholarbank.nus.edu.sg/handle/10635/40599|
|Appears in Collections:||Staff Publications|
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