Semantic Annotation for the LingvoSemantics Project
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Discriminative training for near-synonym substitution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Hybrid semantic analysis system - ATIS data evaluation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Learning conditional random fields from unaligned data for natural language understanding
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Biomedical events extraction using the hidden vector state model
Artificial Intelligence in Medicine
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
| Hi-index | 0.00 |
In this paper, we discuss how discriminative training can be applied to the Hidden Vector State (HVS) model in different task domains. The HVS model is a discrete Hidden Markov Model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, Maximum Likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the ATIS data, and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31% in F-measure when compared with MLE on the DARPA Communicator data and 9% on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4% in F-measure is achieved on the GENIA corpus.