Paper Info
Title
NeuroTPR: A neuro-net toponym recognition model for extracting locations from social media messages.
Abstract
Social media messages, such as tweets, are frequently used by people during natural disasters to share real-time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro-net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models.
Year
DOI
Venue
2020
10.1111/tgis.12627
TRANSACTIONS IN GIS
DocType
Volume
Issue
Journal
24.0
3.0
ISSN
Citations 
PageRank 
1361-1682
3
0.38
References 
Authors
0
3
Name
Order
Citations
PageRank
Jimin Wang141.06
Yingjie Hu241739.76
Kenneth Joseph395.91