To capture this intuition, one solution is to assume that posts published within the same short time window follow the same topic distribution .

Our model is based on the following two assumptions: (1) If a post is about a global event, it is likely to follow a global topic distribution that is time-dependent.

(2) If a post is about a personal topic, it is likely to follow a personal topic distribution that is more or less stable over time.

In standard LDA, a document contains a mixture of topics, represented by a topic distribution , and each word has a hidden topic label.

As we will see very soon, this treatment also allows us to model topic distributions at time window level and user level.

To model this observation, we assume that there is a global topic distribution 6’ for each time point t. Presumably 6’ has a high probability for a topic that is popular in the microblog-sphere at time 75.

(2004) expand topic distributions from document-level to user-level in order to capture users’ specific interests.

We use these topic distributions to compute topic-dependent lexical weighting probabilities and directly incorporate them into our translation model as features.

In our case, by building a topic distribution for the source side of the training data, we abstract the notion of domain to include automatically derived subcorpora with probabilistic membership.

This topic model infers the topic distribution of a test set and biases sentence translations to appropriate topics.

Given a parallel training corpus T composed of documents di, we build a source side topic model over T, which provides a topic distribution p(zn|d7;) for Zn 2 {1, .

Then, we assign p(zn|d7;) to be the topic distribution for every sentence 303- 6 di, thus enforcing topic sharing across sentence pairs in the same document instead of treating them as unrelated.

Computing the topic distribution over a document and assigning it to the sentences serves to tie the sentences together in the document context.

We associate each synchronous rule with a topic distribution, and select desirable rules according to the similarity of their topic distributions with given documents.

The first one relates to the document-topic distribution, which records the topic distribution of each document.

Consequently, we propose a topic similarity model for hierarchical phrase-based translation (Chiang, 2007), where each synchronous rule is associated with a topic distribution .

0 Given a document to be translated, we calculate the topic similarity between a rule and the document based on their topic distributions .

0 We estimate the topic distribution for a rule based on both the source and target side topic models (Section 4.1).

Therefore, we use topic distribution to describe the relatedness of rules to topics.

Figure 1 shows four synchronous rules (Chiang, 2007) with topic distributions , some of which contain nonterminals.

We can see that, although the source part of rule (b) and (c) are identical, their topic distributions are quite different.

* Base—MCM: Our first version injects an informative prior for domain, dialog act and slot topic distributions using information extracted from only labeled training utterances and inject as prior constraints (corpus n-gram base measure during topic assignments.

In the topic model literature, such constraints are sometimes used to deterministically allocate topic assignments to known labels (Labeled Topic Modeling (Ramage et al., 2009)) or in terms of pre-learnt topics encoded as prior knowledge on topic distributions in documents (Reisinger and Pasca, 2009).

* Web n-Gram Context Base Measure (2%): As explained in §3, we use the web n-grams as additional information for calculating the base measures of the Dirichlet topic distributions .

Given n topics, we obtain a topic distribution matrix D using Latent Dirichlet Allocation (Blei et al., 2003).

Consider a user with a topic preference vector t and topic distribution matrix D. We calculate the response probability 1' for all tweets for this user as:

,rw]1xw, where w<|VM| for a given user and the topic distribution matrix D, our task is estimate the topic preference vector t. We do this using maximum-likelihood