Abstract
In sequence labelling, when the label of a token in the sequence is changed, the output probability of the other tokens in the same sequence would also change. We propose a new active learning framework for sequence labelling which take the change of probability into account. At each iteration of the proposed method, every time the human annotator manually annotates a token, the output probabilities of the other tokens in the sequence are re-estimated. This proposed method is expected to reduce the amount of human annotation required for obtaining a high labelling performance. Through experiments on the NP chunking dataset provided by CoNLL, we empirically show that the proposed method works well.
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Wanvarie, D., Takamura, H., Okumura, M. (2010). Active Learning for Sequence Labelling with Probability Re-estimation. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_69
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DOI: https://doi.org/10.1007/978-3-642-15246-7_69
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