A Statistical Model for Identifying Grammatical Relations in Korean Sentences

Songwook LEE

A Statistical Model for Identifying Grammatical Relations in Korean Sentences

Songwook LEE

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This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the grammatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1%, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.12 pp.2863-2871

Publication Date: 2004/12/01

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Type of Manuscript: PAPER

Category: Natural Language Processing

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Songwook LEE, "A Statistical Model for Identifying Grammatical Relations in Korean Sentences" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 12, pp. 2863-2871, December 2004, doi: .
Abstract: This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the grammatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1%, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_12_2863/_p

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@ARTICLE{e87-d_12_2863,
author={Songwook LEE, },
journal={IEICE TRANSACTIONS on Information},
title={A Statistical Model for Identifying Grammatical Relations in Korean Sentences},
year={2004},
volume={E87-D},
number={12},
pages={2863-2871},
abstract={This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the grammatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1%, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - A Statistical Model for Identifying Grammatical Relations in Korean Sentences
T2 - IEICE TRANSACTIONS on Information
SP - 2863
EP - 2871
AU - Songwook LEE
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2004
AB - This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the grammatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1%, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.
ER -