Geographic entity relationship extraction model based on improved CasRel

Geographic entity relationship extraction is a vital task in the field of Geographic Information Science (GIS) and Natural Language Processing (NLP) that aims to extract relationships between geographic entities from text. The current field of geographic entity relationship extraction lacks a public corpus and the existing models face limitations in addressing long-distance relationship modeling and relationship diversity. Therefore, in this study, we first construct the GeoRelCorpus, a corpus encompassing a wide range of geographic entities and relationships. It is based on the Encyclopedia of China Geography branch, and supplemented with the tagging information of OpenStreetMap. The primary objective is to facilitate the training and evaluation of geographic entity relationship extraction models. In terms of model design, we propose an enhanced CasRel model that integrates a multi-scale feature extraction module, combining IDCNN, BiLSTM, and SENet components to improve feature extraction capability and extraction accuracy. Finally, experiments are conducted on the Baidu entity-relationship extraction dataset and GeoRelCorpus. The results demonstrate a notable enhancement in the F1 value achieved by our improved model, thus confirming its effectiveness.

• Publication year

  2024

• Venue

  Other Conferences

• Publication date

  2024-11-12

• Fields of study

  Geography, Computer Science, Engineering

• Identifiers
  DOI 10.1117/12.3049205
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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