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» Learning Probabilistic Models of Word Sense Disambiguation
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EMNLP
2008
14 years 11 months ago
Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk syst...
Rion Snow, Brendan O'Connor, Daniel Jurafsky, Andr...
PAMI
2012
13 years 9 days ago
Unsupervised Learning of Categorical Segments in Image Collections
Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a fle...
Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona
JODS
2006
186views Data Mining» more  JODS 2006»
14 years 9 months ago
Emergent Semantics from Folksonomies: A Quantitative Study
Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology prov...
Lei Zhang 0007, Xian Wu, Yong Yu
KDD
2008
ACM
244views Data Mining» more  KDD 2008»
15 years 10 months ago
Probabilistic latent semantic visualization: topic model for visualizing documents
We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-d...
Tomoharu Iwata, Takeshi Yamada, Naonori Ueda
FCSC
2010
238views more  FCSC 2010»
14 years 7 months ago
Knowledge discovery through directed probabilistic topic models: a survey
Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden str...
Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad