Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff  Home/Search
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Abstract: When applying text learning algorithms to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents bootstrapping as an alternative approach to learning from large sets of labeled data. Instead of a large quantity of labeled data, this paper advocates using a small amount of seed information and a large collection of easily-obtained unlabeled data. Bootstrapping initializes a learner with the seed information; ... (Update)
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BibTeX entry: (Update)
Jones, R., McCallum, A., Nigam, K., & Riloff, E. (1999). Bootstrapping for text learning tasks. Working Notes of the IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications (pp. 52--63). http://citeseer.ist.psu.edu/jones99bootstrapping.html More
@inproceedings{ jones99bootstrapping,
author = "Rosie Jones and Andrew McCallum and Kamal Nigam and Ellen Riloff",
title = "Bootstrapping for Text Learning Tasks",
booktitle = "IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications",
year = 1999,,
url = "citeseer.ist.psu.edu/jones99bootstrapping.html" }
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