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efficient support vector classifiers for named entity recognition named entity (ne) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories such as person, organization, and date. it is a key technology of information extraction and open-domain question answering. first, we show that an ne recognizer based on support vector machines (svms) gives better scores than conventional systems. however, off-the-shelf svm classifiers are too inefficient for this task. therefore, we present a method that makes the system substantially faster. this approach can also be applied to other similar tasks such as chunking and part-of-speech tagging. we also present an svm-based feature selection method and an efficient training method. we propose kernel expansion that is used to transform the d-degree polynomial kernel based classifier into a linear one, with a modified decision function. we propose an xqk (expand the quadratic kernel) which can make their named-entity recognizer drastically fast. |