@inproceedings{baldridge2004ala,
        title = {{Active learning and the total cost of annotation}},
        author = {J. Baldridge and M. Osborne},
        journal = {Proceedings of EMNLP 2004},
        pages = {9--16},
        year = 2004,
        url = {http://www.iccs.informatics.ed.ac.uk/~osborne/papers/emnlp04.pdf},
        abstract = {Active learning (AL) promises to reduce the cost of annotating labeled datasets for trainable human language technologies.
Contrary to expectations, when creating labeled training material for HPSG parse selection and later reusing it with other models, gains from AL may be negligible or even negative. 
This has serious implications for using AL, showing that additional cost-saving strategies may need to be adopted. We explore one such strategy: using a model during annotation to automate some of the decisions. Our best results show an 80% reduction in annotation cost compared with labeling randomly selected data with a single model.},
	biburl = {http://www.bibsonomy.org/bibtex/205587eeedc571dc7b442a00a3593caac/flawed},
	keywords = {annotation active_learning}
}

@article{hachey2005ies,
        title = {{Investigating the Effects of Selective Sampling on the Annotation Task}},
        author = {B. Hachey and B. Alex and M. Becker},
        journal = {Proceedings of CoNLL 2005, Ann Arbor, USA},
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/2c3f791fa04e04a8f497eda1a411abb6d/flawed},
	keywords = {corpus annotation active_learning}
}

@article{vlachos2006aa,
        title = {{Active Annotation}},
        author = {A. Vlachos},
        journal = {Proceedings of the EACL 2006 Workshop on Adaptive Text Extraction},
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2c048ff8bb3d18f6837b6c31160bb2551/flawed},
	keywords = {active_learning corpus annotation}
}

@misc{ieKey,
        title = {A Distributed Architecture for Interactive Parse Annotation},
        address = {Sydney, Australia},
        author = {Baden Hughes and James Haggerty and Joel Nothman and Saritha Manickam and James Curran},
        editor = {Timothy Baldwin and James Curran and Menno van Zaanen},
        journal = {Proceedings Australasian Language Technology Workshop 2005(ALTW 2005)},
        pages = {207-214},
        year = 2005,
        location = {Sydney, Australia},
	abstract = {In this paper we describe a modular system architecture for distributed parse annotation using interactive correction. This involves interactively adding constraints to an existing parse until the returned parse is correct. Using a mixed initiative approach, human annotators interact live with distributed CCG parser servers through an annotation gui. The examples presented to each annotator are selected by an active learning framework to maximise the value of the annotated corpus for machine learners. We report on an initial implementation based on a distributed workflow architecture.},
	biburl = {http://www.bibsonomy.org/bibtex/211de38d3732f9ec79fbc5738663c3bdc/flawed},
	keywords = {annotation active_learning}
}