Furthermore, the hyper-parameters for topic probability distribution and word probability distribution in LDA are a=0.5 and [3:05, respectively.

Here, in the case of clustering the documents based on the topic probabilistic distribution by LDA, the topic distribution over documents 6 is changed in every estimation.

To measure the latent similarity among documents, we construct topic vectors with the topic probabilistic distribution , and then adopt the Jensen-Shannon divergence to measures it, on the other hand, in the case of using document vectors we adopt cosine similarity.

After obtaining a collection of refined documents for classification, we adopt LDA to estimate the latent topic probabilistic distributions over the target documents and use them for clustering.

In this study, we use the topic probability distribution over documents to make a topic vector for each document, and then calculate the similarity among documents.

Here, N is the number of all words in the target documents, wmn is the nth word in the m-th document; 6 is the topic probabilistic distribution for the documents, and gb is the word probabilistic distribution for every topic.

While the SENSESPOTTING task has MT utility in suggesting which new domain words demand a new translation, the MOSTFRE-QSENSECHANGE task has utility in suggesting which words demand a new translation probability distribution when shifting to a new domain.

Second, given a source word 3, we use this classifier to compute the probability distribution of target translations (p(t|s)).

Subsequently, we use this probability distribution to define new features for the SENSESPOTTING task.

Entropy is the entropy of the probability distribution : — 2t p(t|s) log p(t|s).

(1996)) models the conditional probability distribution of the label 3/ given features X, p(y|x) via an exponential model of the form:

The task is to assign a probability distribution over the G dialog state hypotheses, plus a meta-hypothesis which indicates that none of the G hypotheses is correct.

Also note that the dialog state tracker is not predicting the contents of the dialog state hypotheses; the dialog state hypotheses contents are given by some external process, and the task is to predict a probability distribution over them, where the probability assigned to a hypothesis indicates the probability that it is correct.

Dialog state tracking can be seen an analogous to assigning a probability distribution over items on an ASR N-best list given speech input and the recognition output, including the contents of the N-best list.

Thus, we get the probability distribution of a phrase pair or the phrase pairs in the dev data across all subcorpora:

To further improve the similarity score, we apply absolute discounting smoothing when calculating the probability distributions p,( f, e).

We carry out the same smoothing for the probability distributions pi(dev).

For each image iw E Iw of concept w, we output an F -dimensional vector containing prediction scores scorea(iw) for attributes a = 1, ...,F. We transform these attribute vectors into a single vector pw 6 [0,1]1XF, by computing the centroid of all vectors for concept w. The vector is normalized to obtain a probability distribution over attributes given w:

Let P E [0, 1]N XF denote a visual matrix, representing a probability distribution over visual attributes for each word.

We can thus compute the probability distribution over associates for each cue.

Shadow thickness and surface height represents the composite probability distribution of all the twelve kernels.

The cluster centers are located at the means of hypotheses including miscellaneous alternatives not intended, thus the estimated probability distribution is, roughly speaking, offset toward the center of WordNet, which is not what we want.

Figure 1: Proposed probability distribution model for context-to-sense mapping space.

denotes the vocabulary probability distribution in the topic k; M denotes the document number; 6m

denotes the topic probability distribution in the document m; Nm denotes the length of m; me

Preiss (2012) transformed the source language topical model to the target language and classified probability distribution of topics in the same language, whose shortcoming is that the effect of model translation seriously hampers the comparable corpora quality.