Multi-Attention-Based Capsule Network for Uyghur Personal Pronouns Resolution
Multi-Attention-Based Capsule Network for Uyghur Personal Pronouns Resolution
复制标题
基于多注意力的维吾尔族人称代词解析胶囊网络
DOI:
10.1109/access.2020.2989665
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发表时间:
2020
期刊:
影响因子:
3.9
通讯作者:
Song Jinmiao
中科院分区:
文献类型:
--
作者:
Yang Qimeng;Yu Long;Tian Shengwei;Song Jinmiao
Anaphora resolution of Uyghur is a challenging task because of complex language structure and limited corpus. We propose a multi-attention based capsule network model for Uyghur personal pronouns resolution, which can obtain the multi-layer and implicit semantic information effectively. Independently recurrent neural network (IndRNN) is applied in this model to achieve the interdependent features with long distance. Moreover, the capsule network can extract richer textual information to improve expression ability. Compared with the single attention-based model which combines Long Short-Term Memory (LSTM), the multi-attention based capsule network can capture multi-layer semantic information through a multi-attention mechanism without using any external parsing results. Experimental results on Uyghur dataset show that our approach surpasses the state-of-the-art models and gets the highest F-score of 83.85%. Meanwhile, our experimental results demonstrate the proposed method can effectively improve the performance of Uyghur personal pronouns resolution.
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DOI:
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