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» A Framework for Multiple-Instance Learning
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CIKM
2010
Springer
13 years 8 months ago
Rank learning for factoid question answering with linguistic and semantic constraints
This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features. The framework enables query...
Matthew W. Bilotti, Jonathan L. Elsas, Jaime G. Ca...
AROBOTS
2011
13 years 4 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
ICMCS
2009
IEEE
103views Multimedia» more  ICMCS 2009»
13 years 7 months ago
Web image retrieval via learning semantics of query image
The performance of traditional image retrieval approaches remains unsatisfactory, as they are restricted by the wellknown semantic gap and the diversity of textual semantics. To t...
Chuanghua Gui, Jing Liu, Changsheng Xu, Hanqing Lu
COLING
2010
13 years 4 months ago
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there i...
Dmitry Davidov, Oren Tsur, Ari Rappoport
ICML
2004
IEEE
14 years 10 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu