基于多注意力机制的维吾尔语人称代词指代消解

针对深度神经网络模型学习照应语和候选先行语的语义信息忽略了每一个词在句中重要程度, 且无法关注词序列连续性关联和依赖关系等问题, 提出一种结合语境多注意力独立循环神经网络(Contextual multi-attention independently recurrent neural network, CMAIR) 的维吾尔语人称代词指代消解方法. 相比于仅依赖照应语和候选先行语语义信息的深度神经网络, 该方法可以分析上下文语境, 挖掘词序列依赖关系, 提高特征表达能力. 同时, 该方法结合多注意力机制, 关注待消解对多层面语义特征, 弥补了仅依赖内容层面特征的不足, 有效识别人称代词与实体指代关系. 该模型在维吾尔语人称代词指代消解任务中的准确率为90.79 %, 召回率为83.25 %, F值为86.86 %. 实验结果表明, CMAIR模型能显著提升维吾尔语指代消解性能.

Anaphora Resolution of Uyghur Personal Pronouns Based on Multi-attention Mechanism

The deep neural network model learns the semantic information of anaphora and candidate antecedent, ignores the importance of each word in the sentence, and cannot pay attention to the continuous association and dependence of the word sequence. This paper proposes a Uyghur personal pronoun anaphora resolution method based on contextual multi-attention independent recurrent neural network (CMAIR). Compared with deep neural networks that rely only on the semantic information of anaphora and candidate antecedent, this method can analyze context relations, mine word sequence dependencies, and improve feature expression ability. At the same time, this method combines the multiattention mechanism, pays attention to the multi-layer semantic features to be resolved, efiectively compensates for the lack of content-level features, and efiectively recognizes the relationship between personal pronouns and entities. The precision rate of this method in the Uyghur personal pronoun anaphora resolution task is 90.79 %, the recall rate is 83.25 %, and the F value is 86.86 %. The experimental results show that the CMAIR model can signiflcantly improve the performance of Uyghur personal pronoun anaphora resolution.