|PyTorch implementation of a sequence labeler (POS taggger).|
|- take words|
|- run though bidirectional GRU|
|- predict labels one word at a time (left to right), using a recurrent neural network "decoder"|
|The decoder updates hidden state based on:|
|- most recent word|
A small demo of a pleasant, yet simple label placement algorithm for densely packed visualizations. The basic idea is to have labels orbit around their target node at a fixed distance, but repeal each other, so that they don't overlap, and orient themselves to the outside of clusters. To support that, labels on the right of their target node are left-aligned, and labels on the left of their target node are right-aligned; in between, we interpolate. In this example, original nodes are fixed, and force layout governs the label placement.
Modified from Moritz Stefaner's Force-based label placement.