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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

Performance for CRFs and HM-SVMs at each iteration.
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Related In: Results  -  Collection


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fig3: Performance for CRFs and HM-SVMs at each iteration.

Mentions: Figure 3 shows the performance of our proposed framework for CRFs and HM-SVMs at each iteration. At each word position, the feature set used for both statistical models consists of the current word and the current part-of-speech (POS) tag. It can be observed that both models achieve the best performance at iteration 8 with an F-measure of 92.95% and 93.18% being achieved using CRFs and HM-SVMs, respectively.


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

Zhou D, He Y - ScientificWorldJournal (2014)

Performance for CRFs and HM-SVMs at each iteration.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Performance for CRFs and HM-SVMs at each iteration.
Mentions: Figure 3 shows the performance of our proposed framework for CRFs and HM-SVMs at each iteration. At each word position, the feature set used for both statistical models consists of the current word and the current part-of-speech (POS) tag. It can be observed that both models achieve the best performance at iteration 8 with an F-measure of 92.95% and 93.18% being achieved using CRFs and HM-SVMs, respectively.

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