Support Vector Machine with Ensemble Tree Kernel for Relation Extraction

Algorithm 1

Input:

() Initial labeled text set I with little syntactic parsing processing

() Unlabeled text set II with large syntactic parsing processing

Output: Label information of text set II: M-II

// The labeled text set I is equally divided into two data set I1 and I2.

,

,

Begin

  Do

// Training two SVMs based on tree kernel function, namely, svm1 and svm2, on I1 and I2, respectively

   svm1.train and I1 → M1.model

   svm2.train and I2 → M2.model

// classify the relations on text set II by using two trained svm models (M1.model and M2.model), and then generate different

classification result on the same text set II, candidate-I1 and candidate-I2

   M1.model and II → candidate-I1

   M2.model and II → candidate-I2

// choose common samples from candidate-I1 and candidate-I2, and generate the new seed set, candidate-I

   candidate-I ≔ candidate-I1 ∩ candidate-I2

    Until complete the task of classification in text set II

  End

  Printf label information of text set II: M-II