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ICML
2001
IEEE
16 years 20 days ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
KDD
2004
ACM
127views Data Mining» more  KDD 2004»
16 years 7 days ago
A generative probabilistic approach to visualizing sets of symbolic sequences
There is a notable interest in extending probabilistic generative modeling principles to accommodate for more complex structured data types. In this paper we develop a generative ...
Peter Tiño, Ata Kabán, Yi Sun
ICCSA
2003
Springer
15 years 5 months ago
A Probabilistic Model for Predicting Software Development Effort
—Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic model...
Parag C. Pendharkar, Girish H. Subramanian, James ...
PODS
2007
ACM
139views Database» more  PODS 2007»
15 years 12 months ago
Management of probabilistic data: foundations and challenges
Many applications today need to manage large data sets with uncertainties. In this paper we describe the foundations of managing data where the uncertainties are quantified as pro...
Nilesh N. Dalvi, Dan Suciu
ACL
2012
13 years 2 months ago
Unsupervised Semantic Role Induction with Global Role Ordering
We propose a probabilistic generative model for unsupervised semantic role induction, which integrates local role assignment decisions and a global role ordering decision in a uni...
Nikhil Garg, James Henserdon