Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
复制标题
通过对非本地信息建模来学习中英文术语的缩写
DOI:
10.1145/2461316.2461317
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发表时间:
2013-06
期刊:
影响因子:
--
通讯作者:
HOUFENG WANG
中科院分区:
文献类型:
--
作者:
XU SUN;NAOAKI OKAZAKI;JUN’ICHI TSUJII;HOUFENG WANG
The present article describes a robust approach for abbreviating terms. First, in order to incorporate non-local information into abbreviation generation tasks, we present both implicit and explicit solutions: the latent variable model and the label encoding with global information. Although the two approaches compete with one another, we find they are also highly complementary. We propose a combination of the two approaches, and we will show the proposed method outperforms all of the existing methods on abbreviation generation datasets. In order to reduce computational complexity of learning non-local information, we further present an online training method, which can arrive the objective optimum with accelerated training speed. We used a Chinese newswire dataset and a English biomedical dataset for experiments. Experiments revealed that the proposed abbreviation generator with non-local information achieved the best results for both the Chinese and English languages.
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DOI:
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发表时间:
2009-07
期刊:
--
影响因子:
--
作者:
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通讯作者:
Xuan Sun;Takuya Matsuzaki;Daisuke Okanohara;Junichi Tsujii
DOI:
10.1109/tpami.2007.1124
发表时间:
2007-10-01
影响因子:
23.6
作者:
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影响因子:
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作者:
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Youngja Park;Roy J. Byrd
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3
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通讯作者:
Hsu CN
DOI:
10.1093/database/bar013
发表时间:
2011
期刊:
Database : the journal of biological databases and curation
影响因子:
--
作者:
Yamamoto Y;Yamaguchi A;Bono H;Takagi T
通讯作者:
Takagi T