Geographic named entity recognition based on multi-dimensional feature learning and model fusion

doi:10.13474/j.cnki.11-2246.2024.0923

Abstract

Abstract: Geographical named entity recognition is the core task of geographic information extraction, which supports the construction of geographic information systems. However, current research on geographic named entity recognition faces two core challenges: Firstly, the scarcity of annotated data in geographic texts makes it difficult for traditional generic models that rely heavily on annotated data to fully capture and recognize all potential named entities in geographic texts.Secondly, the label density of geographic data is relatively sparse, and models often can not distinguish the boundaries of different geographic named entities, thus unable to accurately locate them. In response to the above issues, this study proposes a named entity recognition algorithm AM-NER for geographic text features. Firstly, using Albert for word vector training, this model is a lightweight pre training model for small samples, which can comprehensively learn semantic information in the geographic field.Secondly, a neuron structure named MNER is designed, which is based on the idea of model fusion and utilizes multiple models to learn semantic features from different dimensions, accurately identifying the boundaries of named entities. Compared to previous studies, AM-NER has improved various indicators in the geographic dataset by 2.05%～2.67%.

Key words: geographical named entity recognition, deep learning, feature learning, model fusion

CLC Number:

P208

MA Haoran, WANG Jinhua. Geographic named entity recognition based on multi-dimensional feature learning and model fusion[J]. Bulletin of Surveying and Mapping, 2024, 0(9): 129-134.

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