CSIRO Data61 at the WNUT Geo Shared Task

Gaya K. Jayasinghe · Brian Jin · James McHugh · Bella Robinson · Stephen Wan

17 citations0/0 files storedInternational Conference on Computational Linguistics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Human Mobility and Location-Based Analysis Natural Language Processing Techniques Authorship Attribution and Profiling

Abstract

In this paper, we describe CSIRO Data61’s participation in the Geolocation shared task at the Workshop for Noisy User-generated Text. Our approach was to use ensemble methods to capitalise on four component methods: heuristics based on metadata, a label propagation method, timezone text classifiers, and an information retrieval approach. The ensembles we explored focused on examining the role of language technologies in geolocation prediction and also in examining the use of hard voting and cascading ensemble methods. Based on the accuracy of city-level predictions, our systems were the best performing submissions at this year’s shared task. Furthermore, when estimating the latitude and longitude of a user, our median error distance was accurate to within 30 kilometers.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 17 citations exist upstream.

References

2012Using Social Media to Enhance Emergency Situation Awareness 2021Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice 2021A Multi-Indicator Approach for Geolocalization of Tweets 2013A Stacking-based Approach to Twitter User Geolocation Prediction 2016Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text 2016pigeo: A Python Geotagging Tool

Cited by

2019Locality-adapted kernel densities of term co-occurrences for location prediction of tweets 2018A Unified Neural Network Model for Geolocating 2019A Practical Guide for the Effective Evaluation of Twitter User Geolocation 2019A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.