Identification (biology)

Toponymy

Test set

Knowledge base

Biology

World Wide Web

Philosophy

Artificial intelligence

Management

Economics

Set (abstract data type)

Programming language

Natural language processing

Referent

Sentence

Named-entity recognition

Sequence labeling

Computer science

Linguistics

Task (project management)

Social media

Botany

Artificial neural network

Entity linking

ISPRS International Journal of Geo-Information

Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models.

Geographic Named Entity Recognition by Employing Natural Language Processing and an Improved BERT Model