We propose a simple yet effective text-based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods
Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). ...
A number of natural language processing and text-mining algorithms have been developed to extract th...
Knowledge bases have been used to improve performance in applications ranging from web search and ev...
We propose a method for embedding two-dimensional locations in a continuous vector space using a neu...
Inferring the location of a user has been a valuable step for many applications that leverage social...
Geographical location is vital to geospatial applications like local search and event detection. In ...
Several Web and social media analytics require user geolocation data. Although Twitter is a powerful...
Automatic geolocation of microblog posts from their text content is particularly difficult because m...
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with...
International audienceThe impressive increasing availability of social media posts has given rise to...
Geolocated social media data provides a powerful source of information about place and regional huma...
The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social ...
Research on automatically geolocating social media users has conventionally been based on the text c...
Most NLP applications assume that a particular language is homogeneous in the regions where it is sp...
AbstractGiven a tweet, a machine learning model when after undertaking the training, development and...
Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). ...
A number of natural language processing and text-mining algorithms have been developed to extract th...
Knowledge bases have been used to improve performance in applications ranging from web search and ev...
We propose a method for embedding two-dimensional locations in a continuous vector space using a neu...
Inferring the location of a user has been a valuable step for many applications that leverage social...
Geographical location is vital to geospatial applications like local search and event detection. In ...
Several Web and social media analytics require user geolocation data. Although Twitter is a powerful...
Automatic geolocation of microblog posts from their text content is particularly difficult because m...
We propose a label propagation approach to geolocation prediction based on Modified Adsorption, with...
International audienceThe impressive increasing availability of social media posts has given rise to...
Geolocated social media data provides a powerful source of information about place and regional huma...
The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social ...
Research on automatically geolocating social media users has conventionally been based on the text c...
Most NLP applications assume that a particular language is homogeneous in the regions where it is sp...
AbstractGiven a tweet, a machine learning model when after undertaking the training, development and...
Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). ...
A number of natural language processing and text-mining algorithms have been developed to extract th...
Knowledge bases have been used to improve performance in applications ranging from web search and ev...