We train a second-order CRF using the methods described in Section 3 and report results for a test set consisting of instances l + 1 through n. For each task we define a symmetric similarity function on the task's vertices V , sim : V × V → R, and build the graph based on its values.
denoting the k nearest neighbors (NN) of node u by N k (u), we use the following mutual k-NN criterion to decide which edges to include: Entries in the final edge weight matrix are: We experiment on ten languages.