We propose an automatic machine translation (MT) evaluation metric that calculates a similarity score (based on precision and recall) of a pair of sentences.

Unlike most metrics, we compute a similarity score between items across the two sentences.

The weights (from the edges) of the resulting graph will then be added to determine the final similarity score between the pair of sentences.

To obtain a single similarity score scores for this sentence pair 3, we simply average the three Fmean scores.

Then, to obtain a single similarity score Sim-score for the entire system corpus, we repeat this process of calculating a scores for each system-reference sentence pair 3, and compute the average over all |S | sentence pairs:

In an n-gram bipartite graph, the similarity score , or the weight 212(6) of the edge 6 connecting a system

Our present model makes strong assumptions about the independence of similarity scores .

We represent each distinct keyphrase as a vector of similarity scores computed over the set of observed keyphrases; these scores are represented by s in Figure 2, the plate diagram of our model.1 Modeling the similarity matrix rather than the sur-

1We assume that similarity scores are conditionally independent given the keyphrase clustering, though the scores are in fact related.

Internally, the ranking uses Jensen-Shannon (Lee, 1999) to compute similarity scores between internal representations of seed attributes, on one hand, and each of the candidate attributes, on the other hand.

4) ranking of candidate attributes with respect to each class (e.g., movies), by computing similarity scores between their individual vector representations and the reference vector of the seed attributes.

To this effect, the extraction includes modifications such that only one reference vector is constructed internally from the seed attributes during the third stage, rather one such vector for each class in (Pasca, 2007); and similarity scores are computed cross-class by comparing vector representations of individual candidate attributes against the only reference vector available during the fourth stage, rather than with respect to the reference vector of each class in (Pasca, 2007).