Learning Morpheme Representation for Mongolian Named Entity Recognition
Learning Morpheme Representation for Mongolian Named Entity Recognition
复制标题
学习蒙古语命名实体识别的语素表示
DOI:
10.1007/s11063-019-10044-6
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发表时间:
2019-05
影响因子:
3.1
通讯作者:
Guanglai Gao
中科院分区:
文献类型:
--
作者:
Weihua Wang;Feilong Bao;Guanglai Gao
Traditional approaches to Mongolian named entity recognition heavily rely on the feature engineering. Even worse, the complex morphological structure of Mongolian words made the data more sparsity. To alleviate the feature engineering and data sparsity in Mongolian named entity recognition, we propose a framework of recurrent neural networks with morpheme representation. We then study this framework in depth with different model variants. More specially, the morpheme representation utilizes the characteristic of classical Mongolian script, which can be learned from unsupervised corpus. Our model will be further augmented by different character representations and auxiliary language model losses which will extract context knowledge from scratch. By jointly decoding by Conditional Random Field layer, the model could learn the dependence between different labels. Experimental results show that feeding the morpheme representation into neural networks outperforms the word representation. The additional character representation and morpheme language model loss also improve the performance.
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影响因子:
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通讯作者:
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DOI:
10.18653/v1/p16-2025
发表时间:
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期刊:
arXiv: Computation and Language
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期刊:
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影响因子:
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10.3115/1073336.1073361
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2001-06
期刊:
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影响因子:
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