One possible way of constructing such a graph is simply connecting all nodes and assign edge weights proportionate to the word association scores, such as PMI, or distributional similarity.

In particular, we consider an undirected edge between a pair of arguments a1 and a2 only if they occurred together in the “a1 and a2” or “a2 and a1” coordination, and assign edge weights as: —> —> w(a1 — a2) = CosineSim(ch>,ch>) = 4%—HalH Ha2H

The edge weights in two subgraphs are normalized so that they are in the comparable range.9

2 The performance of graph propagation varies significantly depending on the graph topology and the corresponding edge weights .

Although we employ the same graph propagation algorithm, our graph construction is fundamentally different in that we integrate stronger inductive biases into the graph topology and the corresponding edge weights .

In contrast to previous work on similar graph models we do not learn any edge weights from training data.

We aim to employ a simple and efficient clustering scheme on this graph and therefore choose 1-nearest-neighbor clustering: for every m, we choose as antecedent m’s child n such that the sum of edge weights is maximal and positive.

In contrast to previous models belonging to this class we do not learn any edge weights but perform inference on the graph structure only which renders our model unsupervised.

Nicolae and Nicolae (2006) phrase coreference resolution as a graph clustering problem: they first perform pairwise classification and then construct a graph using the derived confidence values as edge weights .

Furthermore, the edge weights 3(u, 2)) represent pairwise similarity between sentences or comments (e.g., similarity between views expressed in different comments).

The edge weights are then used to define the inter-sentence distance metric d(u, v) for the different dispersion functions.

The edge weights are then normalized across all edges in the

where B C 8% x 8% is a greedy approximate solution of maximum bipartite matching (West, 1999) on a bipartite graph GB 2 (VB 2 (8%, 6%), EB) with edge weights that are defined by T3.

that maximize the sum of the selected edge weights and that do not share a node as their anchor point.

(2010; 2012) leverage two graphs of entities in each language, that are generated from a pair of corpora, with edge weights quantified as the strength of the relatedness of entities.

Let G = (V, E, W) be a graph where V is the set of vertices, E is the set of edges, and W is the edge weight matrix.

Intuitively, the edge weight W(u, 2)) encodes the degree of our belief about the similarity of the soft labeling for nodes u and v. A soft label K, 6 Am“ is a probability vector in (m + 1)-dimensional simplex, where m is the number of possible labels and the additional dimension accounts for the undefined J. label6.

The second term (2) enforces the smoothness of the labeling according to the graph structure and edge weights .