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We propose a novel algorithm, QuIET, for binary classification of texts. The method automatically generates a set of span queries from a set of annotated documents and uses the query set to categorize unlabeled texts. QuIET generates models that are human understandable. We describe the method and evaluate it empirically against Support Vector Machines, demonstrating a comparable performance for a...
We propose a statistical semantic analysis method for Chinese terms. We use words, part-of-speech (POS) tags, word distances, word contexts and the first sememe of a word in HowNet as features to train a Support Vector Machine (SVM) model for analyzing term semantics. The model is used to identify dependencies embedded inside a term. A Conditional Random Field (CRF) model is used afterwards to incorporate...
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