Study on the Chinese Named Entity Recognition Using Small Scale Character Tail Hints

FENG Yuan-yong; SUN Le; ZHANG Da-kun; LI Wen-bo

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Study on the Chinese Named Entity Recognition Using Small Scale Character Tail Hints

更新时间：2025-07-16

- Study on the Chinese Named Entity Recognition Using Small Scale Character Tail Hints
- Acta Electronica Sinica Vol. 36, Issue 9, Pages: 1833-1838(2008)
- 作者机构：
  
  1. 中国科学院软件研究所基础软件工程研究中心,北京,100080
  2. 广州大学计算机学院,广东,广州,510006
  3. 中国科学院研究生院,北京,100049
  4. 中国科学院软件研究所基础软件工程研究中心北京,100080
  5. 广州大学计算机学院广东广州,510006
  6. 中国科学院研究生院北京,100049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.1
- Published：2008
- 稿件说明：
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FENG Yuan-yong, SUN Le, ZHANG Da-kun, et al. Study on the Chinese Named Entity Recognition Using Small Scale Character Tail Hints[J]. Acta Electronica Sinica, 2008, 36(9): 1833-1838.
DOI：

FENG Yuan-yong, SUN Le, ZHANG Da-kun, et al. Study on the Chinese Named Entity Recognition Using Small Scale Character Tail Hints[J]. Acta Electronica Sinica, 2008, 36(9): 1833-1838. DOI：

摘要

本文针对难度最大的两类命名实体(地名和机构名)在条件随机场框架下首次引入了小规模的常用尾字特征.实验表明

该特征与词类特征具有一定的互补性

联合使用可以以较小的训练代价显著提高专有名词的识别性能

特别是机构名的识别精度.该系统在我国863简体命名实体识别评测语料上专名(人名、地名和机构名)总体F1值达88.76%

超过当年最佳系统8.63个百分点.在SIGHAN 2006命名实体识别语料上的结果也居于前列.

Abstract

We propose small-scale-hint-character-list (SSHCL) features for location and organization names under the conditional random fields framework.As experiments show

SSHCL features provide significant gains in precision

especially for organization names

showing complementary property to part-of-speech.It also lowers construction and training cost greatly that a common large scale feature set demands.The overall proper nouns F1 measurement of integrated system on simple Chinese 863 program 2004 NER corpora reaches 88.76%

gaining 8.63% improvement over the best system in the evaluation.The performance on SIGHAN 2006 is also remarkable.

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