多粒度融合的命名实体识别

孙红,王哲

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PDF(8444 KB)
中文信息学报 ›› 2023, Vol. 37 ›› Issue (3) : 123-134.
信息抽取与文本挖掘

多粒度融合的命名实体识别

  • 孙红,王哲
作者信息 +

A Multi-granularity Approach to Named Entity Recognition

  • SUN Hong, WANG Zhe
Author information +
History +

摘要

目前主流的命名实体识别算法都是从词汇增强的角度出发,引入外部词汇信息提升NER模型获取词边界信息的能力,并未考虑到中文字结构信息在此任务中的重要作用。因此,该文提出多粒度融合的命名实体识别算法,同时引入中文字结构与外部词汇信息,通过编码汉字中每个字部件,并使用注意力机制使得文本序列中的字启发式地融合细粒度信息,赋予模型获取中文字形特征的能力。在多个命名实体识别数据集上的实验结果显示,该算法在模型精度以及推理速度方面具有较大优势。

Abstract

The current named entity recognition algorithms are featured by word enhancement, introducing external vocabulary information to determine the word boundary. This paper proposed a multi-granularity information fusion strategy for named entity recognition algorithm. By encoding each word component in Chinese characters with attention to the word sequence, this model has the ability to capture Chinese glyph information. The experimental results on multiple named entity recognition datasets show that the algorithm has clear advantages in model accuracy and inference speed.

关键词

信息抽取 / 中文命名实体识别 / 注意力机制 / 词汇增强 / 中文字形特征

Key words

information extraction / Chinese named entity recognition / attention mechanism / lexicon enhancement / Chinese glyph features

引用本文

导出引用
孙红,王哲. 多粒度融合的命名实体识别. 中文信息学报. 2023, 37(3): 123-134
SUN Hong, WANG Zhe. A Multi-granularity Approach to Named Entity Recognition. Journal of Chinese Information Processing. 2023, 37(3): 123-134

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基金

国家自然科学基金(61472256,61170277,61703277)
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