Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution

Zeyu Zhang, Steven Bethard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology. Then it reranks the candidate entries using a transformer-based neural network that incorporates information from the ontology such as the entry's population. This generate-and-rerank process is applied twice: first to resolve the less ambiguous countries, states, and counties, and second to resolve the remaining location mentions, using the identified countries, states, and counties as context. Our proposed toponym resolution framework achieves state-of-the-art performance on multiple datasets. Code and models are available at https://github. com/clulab/geonorm.

Original languageEnglish (US)
Title of host publicationStarSEM 2023 - 12th Joint Conference on Lexical and Computational Semantics, Proceedings of the Conference
EditorsAlexis Palmer, Jose Camacho-Collados
PublisherAssociation for Computational Linguistics (ACL)
Pages48-60
Number of pages13
ISBN (Electronic)9781959429760
StatePublished - 2023
Event12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023 - Toronto, Canada
Duration: Jul 13 2023Jul 14 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023
Country/TerritoryCanada
CityToronto
Period7/13/237/14/23

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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