A Named Entity Recognition Method Based on Semantic Plane Interpolation

doi:10.19678/j.issn.1000-3428.0070496

Computer Engineering ›› 2026, Vol. 52 ›› Issue (9): 92-101. doi: 10.19678/j.issn.1000-3428.0070496

• Computational Intelligence and Pattern Recognition • Previous Articles Next Articles

A Named Entity Recognition Method Based on Semantic Plane Interpolation

YANG Li¹^,²^,³, CHEN Yanping¹^,²^,³^,*(), HUANG Hui¹^,²^,³, HUANG Ruizhang¹^,²^,³, QIN Yongbin¹^,²^,³

1. Text Computing and Cognitive Intelligence Engineering Research Center of the Ministry of Education, Guizhou University, Guiyang 550025, Guizhou, China
2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China
3. College of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China

Received:2024-10-17 Revised:2024-12-11 Online:2026-09-15 Published:2025-03-07
Contact: CHEN Yanping

基于语义平面插值的命名实体识别方法

杨丽¹^,²^,³, 陈艳平¹^,²^,³^,*(), 黄辉¹^,²^,³, 黄瑞章¹^,²^,³, 秦永彬¹^,²^,³

1. 贵州大学文本计算与认知智能教育部工程研究中心, 贵州贵阳 550025
2. 贵州大学公共大数据国家重点实验室, 贵州贵阳 550025
3. 贵州大学计算机科学与技术学院, 贵州贵阳 550025

通讯作者: 陈艳平
作者简介:
杨丽, 女, 硕士研究生, 主研方向为自然语言处理、命名实体识别
陈艳平(通信作者), 教授、博士
黄辉, 博士研究生
黄瑞章, 教授、博士
秦永彬, 教授、博士
基金资助:
国家自然科学基金(62066007); 国家自然科学基金(62066008); 国家重点研发计划(2023YFC3304500); 贵州省科技重大专项(黔科合重大专项字[2024]003)

Abstract

Abstract:

In Named Entity Recognition (NER) methods, the nested semantic structure of named entities is effectively parsed by organizing all spans in a sentence into a planarized sentence representation. However, the spans in planarized sentence representation are abstract representations of phrases of different lengths in the sentence. Additionally, a span can represent either a single word or the entire sentence. This characteristic leads to two issues: insufficient contextual semantic dependency in short spans and loss of semantic information in the compression process for long spans. To address these issues, this study proposes a NER method based on semantic plane interpolation. This method expands the sentence plane representation by performing nearest neighbor interpolation on adjacent spans in the plane, which can significantly amplify the semantic features and contextual dependency between spans, effectively building up the semantic dependency between short spans and enhancing the semantic expression of long spans, thus better supporting named entities. Additionally, to avoid losing the original plane information, this study constructs a semantic perception network to extract and fuse global and local information in the original sentence plane representation, achieving fine-grained feature expression through multilayer refinement. The method achieves the highest F1 values of 88.87%, 88.52%, 81.55%, 96.49%, and 72.84% on the ACE2004, ACE2005, GENIA, Resume, and Weibo datasets, respectively, compared with the benchmark models, with improvements of 1.13, 1.61, 0.15, 0.37, and 0.46 percentage points, respectively. These results validate the generality and effectiveness of the proposed method for NER tasks.

Key words: Named Entity Recognition (NER), planarized sentence representation, nearest neighbor interpolation, semantic perception network, semantic plane expansion

摘要：

在命名实体识别(NER)方法中, 通过将句子中的所有跨度表示组织成平面化句子表示, 可以有效解析命名实体的嵌套语义结构。然而, 平面化句子表示中的跨度是句子中不同长度短语的抽象表示, 一个跨度既可能表示单个字, 也可能表示整个句子。这种特性导致短跨度中上下文语义依赖不足和长跨度的语义信息在压缩过程中损失的问题。针对上述问题, 提出一种基于语义平面插值的命名实体识别方法。该方法通过对平面中的相邻跨度进行最近邻插值, 实现句子平面的扩张。这种扩张策略可以显著放大跨度间的语义特征及其上下文依赖关系, 有效构建短跨度之间的语义依赖, 同时增强长跨度中的语义表达, 从而更好地支撑命名实体。此外, 为避免原始平面信息的丢失, 构建一个语义感知网络, 用于提取并融合原始句子平面表示中的全局和局部信息, 实现特征表达的多层细化。该方法在ACE2004、ACE2005、GENIA、简历和微博数据集上的F1值最高分别达到88.87%、88.52%、81.55%、96.49%和72.84%, 与基准模型相比分别提升了1.13、1.61、0.15、0.37和0.46百分点, 验证了该方法在命名实体识别任务中的通用性和有效性。

关键词: 命名实体识别, 平面化句子表示, 最近邻插值, 语义感知网络, 语义平面扩张

YANG Li, CHEN Yanping, HUANG Hui, HUANG Ruizhang, QIN Yongbin. A Named Entity Recognition Method Based on Semantic Plane Interpolation[J]. Computer Engineering, 2026, 52(9): 92-101.

杨丽, 陈艳平, 黄辉, 黄瑞章, 秦永彬. 基于语义平面插值的命名实体识别方法[J]. 计算机工程, 2026, 52(9): 92-101.

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Figures/Tables 10

Fig.1 The overall model architecture

Fig.2 An example of planarized sentence representation

Fig.3 An example of semantic plane interpolation

Fig.4 Named entity recognition performance with different lengths

References 34

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