Assessing named entity recognition efficacy using diverse geoscience datasets

Sandra Paula Villacorta Chambi, Mark Lindsay, Jens Klump, Neil Francis

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

The development of Knowledge Graphs (KGs) significantly relies on the advancements in Named Entity Recognition (NER), which is often hindered by the limited availability of specialised, labelled datasets. Geoscience researchers are exploring innovative strategies for NER due to the lack of a robust labelled terms corpus. In this work, the efficacy of NER in the automatic generation of KGs is examined, and opportunities for further research are identified.

Original languageEnglish
Title of host publication2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9798350389678
DOIs
Publication statusPublished - 6 Jun 2024
Event2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024 - Wellington, New Zealand
Duration: 8 Apr 202410 Apr 2024

Publication series

Name2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

Conference

Conference2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024
Country/TerritoryNew Zealand
CityWellington
Period8/04/2410/04/24

Fingerprint

Dive into the research topics of 'Assessing named entity recognition efficacy using diverse geoscience datasets'. Together they form a unique fingerprint.

Cite this