Interactive Attention Network for Chinese Address Element Recognition

Yusheng Bi, Lihua Tian, Chen Li
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引用次数: 1

Abstract

Existing Named Entity Recognition (NER) models have achieved good performance, but they have low accuracy in Chinese address element recognition tasks. After analysis, we believe that the boundary information of the address text is more sensitive than the general text, and the sentences are independent of each other, unlike the general paragraph-style text with contextual connections. On the other hand, the previous NER models rarely consider the use of interaction between subtasks to enhance the performance of the NER task.This paper proposes an Interactive Attention Network (IAN) model, which uses boundary-based information and type-based information to improve NER task performance and introduces an interaction mechanism to share information between each subtask. In addition, a boundary auxiliary module is added to obtain explicit boundary information. The experimental results show that the proposed IAN model can solve the address element recognition task more effectively.
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中文地址元识别的交互式注意网络
现有的命名实体识别(NER)模型已经取得了较好的性能,但在中文地址元素识别任务中准确率较低。经过分析,我们认为地址语篇的边界信息比一般语篇更敏感,句子之间是相互独立的,不像一般段落式语篇那样具有上下文联系。另一方面,以前的NER模型很少考虑使用子任务之间的交互来提高NER任务的性能。本文提出了一种交互式注意网络模型,该模型利用基于边界的信息和基于类型的信息来提高NER任务的性能,并引入了一种交互机制来实现各子任务之间的信息共享。此外,还增加了边界辅助模块,以获取明确的边界信息。实验结果表明,该模型能更有效地解决地址元识别问题。
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