CSMNER: A Toponym Entity Recognition Model for Chinese Social Media

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuyang Qi, Renjian Zhai, Fang Wu, Jichong Yin, Xianyong Gong, Li Zhu, Haikun Yu
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引用次数: 0

Abstract

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks.
CSMNER: 面向中文社交媒体的地名实体识别模型
在信息爆炸的时代,中文社交媒体已成为海量地理信息的宝库,但其独特的非结构化特性和多样化的表达方式对地名实体识别提出了挑战。针对这一问题,我们提出了中文社交媒体命名实体识别(CSMNER)模型,以提高中文社交媒体文本中地名识别的准确性和鲁棒性。本研究将 BERT(来自变换器的双向编码器表征)预训练模型与改进的 IDCNN-BiLSTM-CRF(迭代稀释卷积神经网络-双向长短期记忆-条件随机场)架构相结合,创新性地加入了边界扩展模块,有效地提取了地名的局部边界特征和上下文语义特征,成功地解决了噪声干扰和语言表达变异带来的识别难题。为了验证模型的有效性,我们在三个数据集上进行了实验:为了验证模型的有效性,我们在三个数据集上进行了实验:微博NER、MSRA和中国社会命名实体识别(CSNER)数据集(一个自建的命名实体识别数据集)。与现有模型相比,CSMNER 在地名识别任务中取得了显著的性能提升。
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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