Figure 4 shows MAP transition Dirichlet hyperparameters of the CLUST model, when trained

Finally, we examine the relationship between the induced clusters and language families in Table 3, for the trigram consonant vs. vowel CLUST model with 20 clusters.

EM 93.37 74.59 ft SYMM 95.99 80.72 MERGE 97.14 86.13 CLUST 98.85 89.37 33 EM 94.50 74.53 E SYMM 96.18 78.13 E MERGE 97.66 86.47 CLUST 98.55 89.07 .5 EM 92.93 78.26 3 SYMM 95.90 79.04 g MERGE 96.06 83.78 2 CLUST 97.03 85.79

Finally, we consider the full version of our model, CLUST , with 20 language clusters.

Figure 3: Confusion matrix for CLUST (left) and EM (right).

Both MERGE and CLUST break symmetries over tags by way of the asymmetric posterior over transition Dirichlet parameters.

We evaluate our methods in two quantitative ways by measuring the degree to which we recover two different sets of gold-standard clusterings .

To measure the similarity between the two clusterings of movie characters, gold clusters Q and induced latent persona clusters C, we calculate the variation of information (Meila, 2007):

VI measures the information-theoretic distance between the two clusterings : a lower value means greater similarity, and VI = 0 if they are identical.

Finally, we consider mention properties derived from unsupervised clusterings ; these properties are designed to target semantic properties of nominals that should behave more like the oracle features than the phi features do.

We consider clusterings that take as input pairs (n, 7“) of a noun head n and a string 7“ which contains the semantic role of n (or some approximation thereof) conjoined with its governor.

We use four different clusterings in our experiments, each with twenty clusters: dependency-parse-derived NAIVEBAYES clusters, semantic-role-derived CONDITIONAL clusters, SRL-derived NAIVEBAYES clusters generating a NOVERB token when 7“ cannot be determined, and SRL-derived NAIVEBAYES clusters with all pronoun tuples discarded.

Their system could be extended to handle property information like we do, but our system has many other advantages, such as freedom from a pre-specified list of entity types, the ability to use multiple input clusterings , and discriminative projection of clusters.

In the True Clusterings setting, we use the annotations to create perfect partitions of the DAs for input to the system; in the System

Clusterings setting, we employ a hierarchical ag-glomerative clustering algorithm used for this task in (Wang and Cardie, 2011).

Table 3 indicates that, with both true clusterings and system clusterings , our system trained on out-of—domain data achieves comparable performance with the same system trained on in-domain data.

We randomly select 15 decision and 15 problem DA clusters (true clusterings ).

This joint minimization for the clusterings for both languages clearly has no benefit since the two terms of the objective are independent.

Using this weighted vocabulary alignment, we state an objective that encourages clusterings to have high average mutual information when alignment links are followed; that is, on average how much information does knowing the cluster of a word cc 6 E impart about the clustering of y E Q, and vice-versa?

We compare two different clusterings of a two-sentence Arabic-English parallel corpus (the English half of the corpus contains the same sentence, twice, while the Arabic half has two variants with the same meaning).

The key idea is to use the latent clusterings to take the place of WordNet semantic classes.

Where the latent clusterings are automatically derived from distributional data based on EM algorithm.

Recently, more sophisticated methods are innovated for SP based on topic models, where the latent variables (topics) take the place of semantic classes and distributional clusterings (Seaghdha, 2010; Ritter et al., 2010).