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Neural Embeddings for Populated Geonames Locations
Published on Nov 28, 2017745 Views
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- muni
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Chapter list
Neural Embeddings for Populated GeoNames Locations00:00
Motivation: feature extraction from locations00:08
Machine learning applications01:42
Motivation: feature extraction from locations03:53
What makes for a ‘good’ feature space?04:30
Do lat-long points capture proximity semantics?07:04
More formally...07:46
Do lat-long points capture proximity semantics?08:49
Do lat-long points capture proximity semantics? - 109:26
What makes for a good feature space?10:25
Idea: ‘Embed’ Geonames as a weighted, directed network...11:03
Step 1: Determine set of nodes in network13:35
Step 2: Determine edges and weights14:21
Step 3: Run DeepWalk on network15:25
Example in paper: North Dakota15:54
Vectors, code and raw data all on GitHub (also, figshare)16:25