一种基于SVM/RS的中文机构名称自动识别方法
A Method of Automatic Recognition for Chinese Organization Name Based on SVM/RS
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摘要: 该文提出一种支持向量机(Support Vector Machines，SVM)和粗糙集(Rough Set, RS)相结合的中文机构名称短语识别方法。该方法借助词的基本语义搭配关系表示短语的构成规则，并通过粗糙集属性约简的方法自动学习到机构名称构成规则的无冗余集。识别时，首先寻找到与这些规则匹配的词串作为候选机构名，然后结合候选机构名以及其上下文词的语义特征，利用SVM分类器判断该候选是否是真正的机构名称。这种方法对1617万字人民日报语料开放测试的F值分别达到82.06%。Abstract: A method to identify Chinese organization names by utilizing SVM (Support Vector Machines) and RS (Rough Set) is provided. Forming rule of organization name is defined based on semanteme collocation relation, and then the un-redundancy set of rough forming rules can be learned by employing attribute reduction in RS automatically. A chain of words matching forming rule is selected first as candidate, then a SVM classifier discern whether a candidate is real organization name according to candidate semanteme and its contextual semanteme while recognizing. Results of open testing achieve F-measure 82.06% in 16.17 million words news based on this project separately.
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