Abstract
Semantic parsing is the task of mapping a natural sentence to a meaning representation. The limitation of semantic parsing is that it is very difficult to obtain annotated training data in which a sentence is paired with a semantic representation. To deal with this problem, we introduce a semi supervised learning model for semantic parsing with ambiguous supervision. The main idea of our method is to utilize a large amount of data, to enrich feature space with the maximum entropy model using our semantic learner. We evaluate the proposed models on standard corpora to show that our methods are suitable for semantic parsing problem. Experimental results show that the proposed methods work efficiently and well on ambiguous data and it is comparable to the state of the art method.
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Nguyen, L.M., Shimazu, A. (2012). A Semi Supervised Learning Model for Mapping Sentences to Logical form with Ambiguous Supervision. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds) Natural Language Processing and Information Systems. NLDB 2012. Lecture Notes in Computer Science, vol 7337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31178-9_11
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DOI: https://doi.org/10.1007/978-3-642-31178-9_11
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