Neural Embeddings for Populated Geonames Locations
published: Nov. 28, 2017, recorded: November 2017, views: 9
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The application of neural embedding algorithms (based on architectures like skip-grams) to large knowledge bases like Wikipedia and the Google News Corpus has tremendously benefited multiple com- munities in applications as diverse as sentiment analysis, named entity recognition and text classification. In this paper, we present a similar resource for geospatial applications. We systematically construct a weighted network that spans all populated places in Geonames. Using a network embedding algorithm that was recently found to achieve excellent results and is based on the skip-gram model, we embed each populated place into a 100-dimensional vector space, in a similar vein as the GloVe embeddings released for Wikipedia. We demonstrate potential applications of this dataset resource, which we release under a public license.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !