Traditional methods for named entity classification are based on hand-coded grammars, lists of trigger words and gazetteers. While these methods have acceptable accuracies they present a serious draw-back: if we need a wider coverage of named entities, or a more domain specific coverage we will probably need a lot of human effort to redesign our grammars and revise the lists of trigger words or gazetteers. We present here a method for improving the accuracy of a traditionally-built named entity extractor. Support vector machines are used to train a classifier based on the output of an existing extractor system. Experimental results show that this approach can be a very practical solution, increasing precision by up to 11.94% and recall by up to 27.83% without considerable human effort. © Springer-Verlag 2004.
CITATION STYLE
Solorio, T., & Lopez, A. L. (2004). Learning named entity classifiers using support vector machines. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2945, 158–167. https://doi.org/10.1007/978-3-540-24630-5_19
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