Chinese Named Entity Recognition using Support Vector Machines

Named entity recognition (NER) is low-level semantics technology. Since it is simple and efficient, it has been widely applied in many systems such as machine translation, information retrieval, information extraction, question answering and summarization. The goal of named entity recognition is to classify names into some particular categories from text, such as the names of people, places, and organizations. Previous studies focus on combining abundant rules or trigger words to enhance the system performance. These methods require domain experts to build up the rules and word set. In this paper, we present a robust named entity recognition system based on support vector machines (SVM). In the experiment, we perform the one-against-one SVM algorithm and a feature extraction method to achieve high accuracy