The basic idea of these models is to compare the topic distribution of a target instance with the candidate sense paraphrases and choose the most probable one.

We then compare the topic distributions of literal and nonliteral senses.

As the topic distribution of nouns and verbs exhibit different properties, topic comparisons across parts-of-speech do not make sense.

make the topic distributions comparable by making sure each type of paraphrase contains the same sets of parts-of-speech.

In this paper, we propose a novel framework which is fairly resource-poor in that it requires only 1) a large unlabelled corpus from which to estimate the topics distributions , and 2) paraphrases for the possible target senses.

In addition to generating a topic from the document’s topic distribution and sampling a word from that topic, the enhanced model also generates a distributional neighbour for the chosen word and then assigns a sense based on the word, its neighbour and the topic.

A similar topic distribution to that of the individual words ‘norm’ or ‘trouble’ would be strong supporting evidence of the corresponding idiomatic reading.).

An alternative yet feasible solution, presented in this work, is building a model that can summarize new document clusters using characteristics of topic distributions of training documents.

Such models can yield comparable or better performance on DUC and other evaluations, since representing documents as topic distributions rather than bags of words diminishes the effect of lexical variability.

Our focus is on identifying similarities of candidate sentences to summary sentences using a novel tree based sentence scoring algorithm, concerning topic distributions at different levels of the discovered hierarchy as described in § 3 and § 4,

We discover hidden topic distributions of sentences in a given document cluster along with provided summary sentences based on hLDA described in (Blei et al., 2003a)1.

We build a summary-focused hierarchical probabilistic topic model, sumHLDA, for each document cluster at sentence level, because it enables capturing expected topic distributions in given sentences directly from the model.

Each node is associated with a topic distribution over words.

age and a sentence can be broadly measured by the extent to which they share the same topic distributions (Steyvers and Griffiths, 2007).

K 1309,61): 2 pjlogz Ii (4) 1:1 ‘11 where p and q are shorthand for the image topic distribution PdMix and sentence topic distribution PSd, respectively.

The image annotation model takes the topic distributions into account when finding the most likely keywords for an image and its associated document.

By simultaneously inferring latent topics and topic distributions over relations, LDA-SP combines the benefits of previous approaches: like traditional class-based approaches, it produces human-interpretable classes describing each relation’s preferences, but it is competitive with non-class-based methods in predictive power.

ing related topic pairs between arguments we employ a sparse prior over the per-relation topic distributions .

Finally we note that, once a topic distribution has been learned over a set of training relations, one can efficiently apply inference to unseen relations (Yao et al., 2009).