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CIKM
2009
Springer
15 years 9 months ago
Semi-supervised learning of semantic classes for query understanding: from the web and for the web
Understanding intents from search queries can improve a user’s search experience and boost a site’s advertising profits. Query tagging via statistical sequential labeling mode...
Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szyma...
EMNLP
2008
15 years 4 months ago
Modeling Annotators: A Generative Approach to Learning from Annotator Rationales
A human annotator can provide hints to a machine learner by highlighting contextual "rationales" for each of his or her annotations (Zaidan et al., 2007). How can one ex...
Omar Zaidan, Jason Eisner
126
Voted
IJCAI
2007
15 years 4 months ago
Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions
We describe an approach to extract attribute-value pairs from product descriptions. This allows us to represent products as sets of such attribute-value pairs to augment product d...
Katharina Probst, Rayid Ghani, Marko Krema, Andrew...
CVPR
2000
IEEE
16 years 4 months ago
Learning from One Example through Shared Densities on Transforms
We define a process called congealing in which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based ...
Erik G. Miller, Nicholas E. Matsakis, Paul A. Viol...
EUROMICRO
1997
IEEE
15 years 6 months ago
What computer architecture can learn from computational intelligence-and vice versa
This paper considers whether the seemingly disparate fields of Computational Intelligence (CI) and computer architecture can profit from each others’ principles, results and e...
Ronald Moore, Bernd Klauer, Klaus Waldschmidt