Computing the Edit-Distance between Unrooted Ordered Trees
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Parameterization of the input in training the HVS semantic parser
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
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The key component of a spoken dialogue system is a spoken understanding module. There are many approaches to the understanding module design and one of the most perspective is a statistical based semantic parsing. This paper presents a combination of a set of modifications of the hidden vector state (HVS) parser which is a very popular method for the statistical semantic parsing. This paper describes the combination of three modifications of the basic HVS parser and proves that these changes are almost independent. The proposed changes to the HVS parser form the extended hidden vector state parser (EHVS). The performance of the parser increases from 47.7% to 63.1% under the exact match between the reference and the hypothesis semantic trees evaluated using Human-Human Train Timetable corpus. In spite of increased performance, the complexity of the EHVS parser increases only linearly. Therefore the EHVS parser preserves simplicity and robustness of the baseline HVS parser.