Enhancing Performance of Lexical Entailment Recognition for Vietnamese based on Exploiting Lexical Structure Features

Enhancing Performance of Lexical Entailment Recognition for Vietnamese based on Exploiting Lexical Structure Features

The lexical entailment recognition problem aims to identify the is-a relation between words. The problem has recently been receiving research attention in the natural language processing field. In this study, we propose a novel method (VLER) for this problem on Vietnamese. For this purpose, we first...

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Bibliographic Details
Published in:2018 10th International Conference on Knowledge and Systems Engineering (KSE) pp. 341 - 346
Main Authors: Tan, Bui Van, Thai, Nguyen Phuong, Thuan, Nguyen Minh
Format: Conference Proceeding
Language:English
Published: IEEE 01-11-2018
Subjects:
Compounds
hypernymy detection
Knowledge engineering
lexical entailment
lexical entailment recognition
Mathematical model
Modeling
Predictive models
Semantics
taxonomic relation
Training
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Summary:The lexical entailment recognition problem aims to identify the is-a relation between words. The problem has recently been receiving research attention in the natural language processing field. In this study, we propose a novel method (VLER) for this problem on Vietnamese. For this purpose, we first exploit such lexical structure information of words as a feature, then combine this feature with vectors representation of words such as a unique feature for recognizing the relation. Moreover, we applied a number of methods based on word embedding and supervised learning, experimental results showed that our method achieves the best performance in the hypernymy detection task than other methods in terms of accuracy.
DOI:10.1109/KSE.2018.8573391