Exploiting Semantic Annotations and -Learning for Constructing an Efficient Hierarchy/Graph Texts Organization

Algorithm 4

IOAC-QL algorithm.

Input: Feature_Vertex Object(FVO), Wordnet

Output: The optimal action for FVO relationships;

 Procedure

{ intialize ;

 /^*implement -learning algorithm to get the action with the highest -value. /^*

  ∈ action_space = {similarity, contiguity, contrast, causality}

   /^*Parallel.foreach used for parallel processing of all actions. /^*

 Parallel.for(0; action_space.count(); =>

  Get_Action_Reward([]); /^*Function to implement (8). /^*

  *  * ( + γ * ())

   }); // Parallel.For

 return Get_highest_-value_action(); /^*select action with the maximum summed value of -values /^*

 }

/^*execute (8) to get reward value. /^*

Get_Action_Reward( )

 {  = (freq(.val)/FVO.Count()) * log(/);

 for (int = 0; < FVO.Count(); ++)

 {  = (freq(FVO[])/FVO.Count()) * log(/);

  SemRel() = Get_SemRel(.val, FVO[])

  Sum +=  * SemRel();

 }

 return  + Sum;

}