Abstract
When processing a noisy corpus such as clinical texts, the corpus usually contains a large number of misspelt words, abbreviations and acronyms while many ambiguous and irregular language usages can also be found in training data needed for supervised learning. These are two frequent kinds of noise that can affect the overall performance of machine learning process. The first noise is usually filtered by the proof reading process. This paper proposes an algorithm to deal with noisy training data problem, for a method we call reverse active learning to improve performance of supervised machine learning on clinical corpora. The effects of reverse active learning are shown to produce results on the i2b2 clinical corpus that are state-of-the-art of supervised learning method and offer a means of improving all processing strategies in clinical language processing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2010)
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. J. Artif. Intell. 97(1-2), 245–271 (1997)
Fung, G., Mangasarian, O.L.: Data selection for support vector machine classifiers. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 64–70. ACM, New York (2000)
Donmez, P., Carbonell, J.G., Schneider, J.: Efficiently learning the accuracy of labeling sources for selective sampling. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 259–268. ACM, New York (2009)
Nallapati, R., Surdeanu, M., Manning, C.: Corractive learning: Learning from noisy data through human interaction. In: IJCAI Workshop on Intelligence and Interaction (2009)
Lewis, D., Gale, W.: Training text classifiers by uncertainty sampling. In: Proceedings of ACM-SIGIR Conference on Information Retrieval, pp. 3–12 (1994)
Scheffer, T., Decomain, C., Wrobel, S.: Active Hidden Markov Models for Information Extraction. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 309–318. Springer, Heidelberg (2001)
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, pp. 1070–1079. Association for Computational Linguistics, Stroudsburg (2008)
Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 287–294. ACM, New York (1992)
McCallum, A., Nigam, K.: Employing em and pool-based active learning for text classification. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 350–358. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Vlachos, A.: A stopping criterion for active learning. Computer Speech and Language 22(3), 295–312 (2008)
Olsson, F., Tomanek, K.: An intrinsic stopping criterion for committee-based active learning. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, CoNLL 2009, pp. 138–146. Association for Computational Linguistics, Stroudsburg (2009)
Kang, J., Ryu, K., Kwon, H.C.: Using Cluster-Based Sampling to Select Initial Training Set for Active Learning in Text Classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 384–388. Springer, Heidelberg (2004)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 441–448. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Patrick, J., Sabbagh, M., Jain, S., Zheng, H.: Spelling correction in clinical notes with emphasis on first suggestion accuracy. In: 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining, pp. 2–8 (2010)
Lafferty, J., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001)
Cristianini, N., Shawe-Taylor, J.: An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge Univ. Pr. (2000)
Baram, Y., El-Yaniv, R., Luz, K.: Online choice of active learning algorithms. Journal of Machine Learning Research 5, 255–291 (2004)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software http://www.csie.ntu.edu.tw/
Uzuner, O., South, B.R., Shen, S., DuVall, S.L.: 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18(5), 552–556 (2011)
Chih-Wei Hsu, C.C.C., Lin, C.J.: A practical guide to support vector classification. Technical report (2003-2010)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Rink, B., Harabagiu, S., Roberts, K.: Automatic extraction of relations between medical concepts in clinical texts. J. Am. Med. Inform. Assoc. 18(5), 594–600 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, D., Patrick, J. (2012). Reverse Active Learning for Optimising Information Extraction Training Production. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-35101-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35100-6
Online ISBN: 978-3-642-35101-3
eBook Packages: Computer ScienceComputer Science (R0)