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AMT
2006
Springer
147views Multimedia» more  AMT 2006»
13 years 11 months ago
Semi-Supervised Text Classification Using Positive and Unlabeled Data
Text classification using positive and unlabeled data refers to the problem of building text classifier using positive documents (P) of one class and unlabeled documents (U) of man...
Shuang Yu, Xueyuan Zhou, Chunping Li
COLING
2008
13 years 9 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
Gholamreza Haffari, Anoop Sarkar
EMNLP
2009
13 years 5 months ago
Graph Alignment for Semi-Supervised Semantic Role Labeling
Unknown lexical items present a major obstacle to the development of broadcoverage semantic role labeling systems. We address this problem with a semisupervised learning approach ...
Hagen Fürstenau, Mirella Lapata
CVPR
2008
IEEE
14 years 9 months ago
Semi-Supervised Discriminant Analysis using robust path-based similarity
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
Yu Zhang, Dit-Yan Yeung
CVPR
2006
IEEE
14 years 9 months ago
Semi-Supervised Classification Using Linear Neighborhood Propagation
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...