Learning Morpheme Representation for Mongolian Named Entity Recognition

Learning Morpheme Representation for Mongolian Named Entity Recognition
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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
Guanglai Gao
中科院分区:
计算机科学4区
文献类型:
--
作者:
Weihua Wang;Feilong Bao;Guanglai Gao

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蒙古语命名实体识别的传统方法在很大程度上依赖于特征工程。更糟糕的是,蒙古语单词复杂的形态结构使数据更加稀疏。为了缓解……中的特征工程和数据稀疏问题
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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