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Firstly, we propose an SVM with uneven margins (SVMUM) model to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks, where they achieved better performance than the standard SVM and the SVM with passive learning, respectively. Moreover, by combining SVMUM with the active learning algorithm, we achieve the best reported results on the seminars and jobs corpora, which are benchmark data sets used for evaluation and comparison of machine learning algorithms for IE. In addition, we also evaluate the token based classification framework for IE with three different entity tagging schemes. In comparison to previous methods dealing with the same problems, our methods are both effective and efficient, which are valuable features for real-world applications. Due to the similarity in the formulation of the learning problem for IE and for other NLP tasks, the two techniques are likely to be beneficial in a wide range of applications<jats:sup>1<\/jats:sup>.<\/jats:p>","DOI":"10.1017\/s1351324908004968","type":"journal-article","created":{"date-parts":[[2008,12,18]],"date-time":"2008-12-18T10:09:17Z","timestamp":1229594957000},"page":"241-271","source":"Crossref","is-referenced-by-count":45,"title":["Adapting SVM for data sparseness and imbalance: a case study in information extraction"],"prefix":"10.1017","volume":"15","author":[{"given":"YAOYONG","family":"LI","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KALINA","family":"BONTCHEVA","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HAMISH","family":"CUNNINGHAM","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2009,4,1]]},"reference":[{"key":"S1351324908004968_ref41","first-page":"101","article-title":"In defense of one-vs-all classification","volume":"5","author":"Rifkin","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"S1351324908004968_ref58","doi-asserted-by":"crossref","unstructured":"Zhou G. , Su J. , Zhang J. , and Zhang M. 2005. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Annual Meeting of the ACL, pp. 427\u2013434. Association for Computational Linguistics.","DOI":"10.3115\/1219840.1219893"},{"key":"S1351324908004968_ref16","unstructured":"Finn A. , and Kushmerick N. 2003. Active learning selection strategies for information extraction. In ECML-03 Workshop on Adaptive Text Extraction and Mining."},{"key":"S1351324908004968_ref32","unstructured":"Lee Y. , Ng H. , and Chia T. 2004. Supervised word sense disambiguation with support vector machines and multiple knowledge sources. In Proceedings of SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 137\u2013140. Association for Computational Linguistics."},{"key":"S1351324908004968_ref21","doi-asserted-by":"crossref","unstructured":"Gimenez J. , and Marquez L. 2003. Fast and accurate part-of-speech tagging: the SVM approach revisited. 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