The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Analysis system of speech acts and discourse structures using maximum entropy model
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Cascaded grammatical relation-driven parsing using support vector machines
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
| Hi-index | 0.00 |
This study aims to improve the performance of identifying grammatical functions between an adnoun clause and a noun phrase in Korean. The key task is to determine the relation between the two constituents in terms of such functional categories as subject, object, adverbial and appositive. The problem is mainly caused by the fact that functional morphemes, which are considered to be crucial for identifying the relation, are omitted in the noun phrases. To tackle this problem, we propose to employ the Support Vector Machines (SVM) in determining the grammatical functions. Through an experiment with a tagged corpus for training SVMs, we found the proposed model to be more useful than both the Maximum Entropy Model (MEM) and the backed-off model.