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Semi-supervised learning of statistical models for natural language understanding.

Zhou D, He Y - ScientificWorldJournal (2014)

Bottom Line: The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations.Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations.In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.

ABSTRACT
Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

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Related in: MedlinePlus

The proposed learning framework of training statistical models from abstract semantic annotations.
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Related In: Results  -  Collection


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fig1: The proposed learning framework of training statistical models from abstract semantic annotations.

Mentions: We propose a learning framework based on EM to train statistical models from abstract semantic annotations as illustrated in Figure 1. The whole procedure works as follows. Given a set of sentences S = {Si, i = 1,…, N} and their corresponding semantic annotations A = {Ai, i = 1,…, N}, each annotation Ai is expanded to the flattened semantic tag sequence Ci at initialization step. Based on the flattened semantic tag sequences, the initial model parameters are estimated. After that, the semantic tag sequence is generated for each sentence using the current model, . Then, is filtered based on a score function which measures the agreement of the generated semantic tag sequences with the actual flattened semantic tag sequences. In the maximization step, model parameters are reestimated using the filtered . The iteration continues until convergence. The details of each step are discussed in Figure 1.


Semi-supervised learning of statistical models for natural language understanding.

Zhou D, He Y - ScientificWorldJournal (2014)

The proposed learning framework of training statistical models from abstract semantic annotations.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127215&req=5

fig1: The proposed learning framework of training statistical models from abstract semantic annotations.
Mentions: We propose a learning framework based on EM to train statistical models from abstract semantic annotations as illustrated in Figure 1. The whole procedure works as follows. Given a set of sentences S = {Si, i = 1,…, N} and their corresponding semantic annotations A = {Ai, i = 1,…, N}, each annotation Ai is expanded to the flattened semantic tag sequence Ci at initialization step. Based on the flattened semantic tag sequences, the initial model parameters are estimated. After that, the semantic tag sequence is generated for each sentence using the current model, . Then, is filtered based on a score function which measures the agreement of the generated semantic tag sequences with the actual flattened semantic tag sequences. In the maximization step, model parameters are reestimated using the filtered . The iteration continues until convergence. The details of each step are discussed in Figure 1.

Bottom Line: The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations.Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations.In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.

ABSTRACT
Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Show MeSH
Related in: MedlinePlus