• Publications
  • Influence
Active Learning Literature Survey
TLDR
The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer labeled training instances. Expand
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Toward an Architecture for Never-Ending Language Learning
TLDR
We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base, and learn to perform this task better than on the previous day. Expand
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An Analysis of Active Learning Strategies for Sequence Labeling Tasks
TLDR
We survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. Expand
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Active Learning
  • B. Settles
  • Computer Science
  • Active Learning
  • 2 July 2012
TLDR
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. Expand
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Biomedical Named Entity Recognition using Conditional Random Fields and Rich Feature Sets
  • B. Settles
  • Computer Science
  • NLPBA/BioNLP
  • 28 August 2004
TLDR
This paper presents a framework for simultaneously recognizing occurrences of PROTEIN, DNA, RNA, CELL-LINE, and CELLTYPE entity classes using Conditional Random Fields with a variety of traditional and novel features. Expand
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Never-Ending Learning
TLDR
We propose a neverending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. Expand
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ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text
TLDR
A Biomedical Named Entity Recognizer for molecular biology text mining using conditional random fields . Expand
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Multiple-Instance Active Learning
TLDR
We present a framework for active learning in the multiple-instance (MI) setting. Expand
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ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text
  • B. Settles
  • Computer Science, Medicine
  • Bioinform.
  • 2005
TLDR
ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. Expand
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Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances
TLDR
We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, and (2) at least as accurate as preexisting methods. Expand
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