Conditional random field

Voltage

Computer science

F1 score

Systems engineering

Geography

Language model

Natural language processing

Information retrieval

Engineering

Set (abstract data type)

Named entity

Transformer

Entity linking

Archaeology

Metric (unit)

Knowledge base

Artificial intelligence

Information extraction

Programming language

Operations management

Named-entity recognition

Task (project management)

Toponymy

Electrical engineering

International Journal of Geographical Information Science

Place names embedded in online natural language text present a useful source of geographic information. Despite this, many methods for the extraction of place names from text use pre-trained models that were not explicitly designed for this task. Our paper builds five custom-built Named Entity Recognition (NER) models and evaluates them against three popular pre-built models for place name extraction. The models are evaluated using a set of manually annotated Wikipedia articles with reference to the F1 score metric. Our best performing model achieves an F1 score of 0.939 compared with 0.730 for the best performing pre-built model. Our model is then used to extract all place names from Wikipedia articles in Great Britain, demonstrating the ability to more accurately capture unknown place names from volunteered sources of online geographic information.

Transformer based named entity recognition for place name extraction from unstructured text