Paper: A Hybrid Generative/Discriminative Framework to Train a Semantic Parser from an Un-annotated Corpus

ACL ID C08-1140
Title A Hybrid Generative/Discriminative Framework to Train a Semantic Parser from an Un-annotated Corpus
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

We propose a hybrid genera- tive/discriminative framework for se- mantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM- SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract an- notations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are ...