Limits...
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.

Show MeSH

Related in: MedlinePlus

Comparisons of performance with or without the filtering stage.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4127215&req=5

fig5: Comparisons of performance with or without the filtering stage.

Mentions: In a second set of experiments, we compare the performance with or without the filtering step as discussed in Section 3.3. Figure 5 shows that the filtering step is indeed crucial as it boosted the performance by nearly 4% for CRFs with L-BFGS and 3% for CRFs with SGD and HM-SVMs.


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

Zhou D, He Y - ScientificWorldJournal (2014)

Comparisons of performance with or without the filtering stage.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Comparisons of performance with or without the filtering stage.
Mentions: In a second set of experiments, we compare the performance with or without the filtering step as discussed in Section 3.3. Figure 5 shows that the filtering step is indeed crucial as it boosted the performance by nearly 4% for CRFs with L-BFGS and 3% for CRFs with SGD and HM-SVMs.

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