An intermediate vector, δ, is calculated, which will contain the unnormalized log probability that any path through the trellis will pass through a particular state k for the particular word t. The δ vectors have a dimension of N, the number of tags, and they are re-used for the gradient calculation during back-propagation.
recent results using multilayer neural networks and pre-trained word embeddings have demonstrated high performance using a much smaller number of minimalist features.