Multi-Criterion Active Learning in Conditional Random Fields
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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The recent growing interests in Semantic Web trigger the requirements of annotating various information objects (e.g., documents) on the Web. The main drawback of the existing methods is that they usually require many manually annotated training examples as inputs. This paper proposes a SVM-struct based active learning algorithm for automatic semantic annotation. In particular, the proposed algorithm is underpinned by a novel uncertainty minimization method which can identify the most discriminative examples for re-training so as to reduce the manual annotation cost. Our initial experiments show that the proposed method can achieve comparable annotation performance while requiring a much smaller training set.