Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information

Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
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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:
ACM Transactions on Asian Language Information Processing
影响因子:
--
通讯作者:
HOUFENG WANG
HOUFENG WANG
中科院分区:
其他
文献类型:
--
作者:
XU SUN;NAOAKI OKAZAKI;JUN’ICHI TSUJII;HOUFENG WANG

文献摘要

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本文描述了缩写术语的一种强有力的方法。首先,为了将非局部信息融入到缩写生成任务中,我们提出了隐式和显式两种解决方案:潜变量模型和全局信息的标签编码。虽然这两种方法相互竞争,但我们发现它们也具有很强的互补性。我们提出了这两种方法的组合,并且我们将在缩写生成数据集上展示所提出的方法优于所有已有方法。为了降低非局部信息学习的计算复杂度,进一步提出了一种在线训练方法,该方法能在加快训练速度的同时达到目标最优解。我们使用了一个中文新闻通讯社的数据集和一个英文的生物医学数据集进行实验。实验表明,本文提出的非本地信息缩略语生成器对中文和英文都取得了最好的效果。
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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