IEICE TRANSACTIONS on Fundamentals
Chinese Lexical Sememe Prediction Using CilinE Knowledge
Summary :
Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.2 pp.146-153
- Publication Date
- 2023/02/01
- Publicized
- 2022/08/18
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAP1074
- Type of Manuscript
- PAPER
- Category
- Language, Thought, Knowledge and Intelligence
Authors
Hao WANG
North China University of Technology,Beijing Urban Governance Research Center,CNONIX National Standard Application and Promotion Lab
Sirui LIU
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Jianyong DUAN
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Li HE
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Xin LI
North China University of Technology,CNONIX National Standard Application and Promotion Lab
Keyword
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Hao WANG, Sirui LIU, Jianyong DUAN, Li HE, Xin LI, "Chinese Lexical Sememe Prediction Using CilinE Knowledge" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 2, pp. 146-153, February 2023, doi: 10.1587/transfun.2022EAP1074.
Abstract: Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1074/_p
Copy
@ARTICLE{e106-a_2_146,
author={Hao WANG, Sirui LIU, Jianyong DUAN, Li HE, Xin LI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Chinese Lexical Sememe Prediction Using CilinE Knowledge},
year={2023},
volume={E106-A},
number={2},
pages={146-153},
abstract={Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.},
keywords={},
doi={10.1587/transfun.2022EAP1074},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Chinese Lexical Sememe Prediction Using CilinE Knowledge
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 146
EP - 153
AU - Hao WANG
AU - Sirui LIU
AU - Jianyong DUAN
AU - Li HE
AU - Xin LI
PY - 2023
DO - 10.1587/transfun.2022EAP1074
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2023
AB - Sememes are the smallest semantic units of human languages, the composition of which can represent the meaning of words. Sememes have been successfully applied to many downstream applications in natural language processing (NLP) field. Annotation of a word's sememes depends on language experts, which is both time-consuming and labor-consuming, limiting the large-scale application of sememe. Researchers have proposed some sememe prediction methods to automatically predict sememes for words. However, existing sememe prediction methods focus on information of the word itself, ignoring the expert-annotated knowledge bases which indicate the relations between words and should value in sememe predication. Therefore, we aim at incorporating the expert-annotated knowledge bases into sememe prediction process. To achieve that, we propose a CilinE-guided sememe prediction model which employs an existing word knowledge base CilinE to remodel the sememe prediction from relational perspective. Experiments on HowNet, a widely used Chinese sememe knowledge base, have shown that CilinE has an obvious positive effect on sememe prediction. Furthermore, our proposed method can be integrated into existing methods and significantly improves the prediction performance. We will release the data and code to the public.
ER -
English
