Abstract
Authorship attribution refers to the task of identifying the authors of a set of documents. Early studies in this area either used book length texts or assumed that there were a large number of training documents. The focus of modern authorship attribution has been shifted to the analysis on small online texts. This is realistic since in the real life it is hard to collect the training texts. However, the small size of training data makes the authorship attribution much more difficult. In this paper, we present a novel co-training method to iteratively recognize a few unlabeled data to augment the training set. Specifically, each document is first partitioned into two distinct views, i.e., lexical and syntactic view. And then, a two view semi-supervised method, co-training, is adopted to exploit the large amount of unlabeled documents. Our experiment results based on real data show that the proposed method can effectively exploit unlabeled data to improve the classification performance.
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Fan, M., Qian, T., Chen, L., Liu, B., Zhong, M., He, G. (2014). Authorship Attribution with Very Few Labeled Data: A Co-training Approach. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_70
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DOI: https://doi.org/10.1007/978-3-319-08010-9_70
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