Conclusion | 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. |
Experiments | We then compare the topic distributions of literal and nonliteral senses. |
Experiments | As the topic distribution of nouns and verbs exhibit different properties, topic comparisons across parts-of-speech do not make sense. |
Experiments | make the topic distributions comparable by making sure each type of paraphrase contains the same sets of parts-of-speech. |
Introduction | 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. |
Related Work | 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. |
The Sense Disambiguation Model | A similar topic distribution to that of the individual words ‘norm’ or ‘trouble’ would be strong supporting evidence of the corresponding idiomatic reading.). |
Background and Motivation | 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. |
Introduction | 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. |
Introduction | 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, |
Summary-Focused Hierarchical Model | 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. |
Summary-Focused Hierarchical Model | 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. |
Summary-Focused Hierarchical Model | Each node is associated with a topic distribution over words. |
Extractive Caption Generation | age and a sentence can be broadly measured by the extent to which they share the same topic distributions (Steyvers and Griffiths, 2007). |
Extractive Caption Generation | 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. |
Image Annotation | The image annotation model takes the topic distributions into account when finding the most likely keywords for an image and its associated document. |
Abstract | 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. |
Topic Models for Selectional Prefs. | ing related topic pairs between arguments we employ a sparse prior over the per-relation topic distributions . |
Topic Models for Selectional Prefs. | 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). |