Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources
Summary :
In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.2 pp.405-408
- Publication Date
- 2017/02/01
- Publicized
- 2016/11/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8135
- Type of Manuscript
- LETTER
- Category
- Natural Language Processing
Authors
Juryong CHEON
Dong-A University
Youngjoong KO
Dong-A University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Juryong CHEON, Youngjoong KO, "Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 2, pp. 405-408, February 2017, doi: 10.1587/transinf.2016EDL8135.
Abstract: In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8135/_p
Copy
@ARTICLE{e100-d_2_405,
author={Juryong CHEON, Youngjoong KO, },
journal={IEICE TRANSACTIONS on Information},
title={Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources},
year={2017},
volume={E100-D},
number={2},
pages={405-408},
abstract={In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.},
keywords={},
doi={10.1587/transinf.2016EDL8135},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Automatically Extracting Parallel Sentences from Wikipedia Using Sequential Matching of Language Resources
T2 - IEICE TRANSACTIONS on Information
SP - 405
EP - 408
AU - Juryong CHEON
AU - Youngjoong KO
PY - 2017
DO - 10.1587/transinf.2016EDL8135
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2017
AB - In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.
ER -
English
