Let x be a document and wm a weight vector associated with the genre class m in a corpus with k genres at the most fine-grained level.

The predicted class is the class achieving the maximum inner product between x and the weight vector for the class, denoted as,

Accurate prediction requires that when a document vector is multiplied with the weight vector associated with its own class, the resulting inner product should be larger than its inner products with a weight vector for any other genre class m. This helps us to define criteria for weight vectors .

to constrain the hypothesis space, i.e., the space of possible weight vectors , of the target task by considering multiple different but related prediction tasks.

The subspace is used to constrain the learning of the target task by restricting the weight vector w to lie in the subspace defined by 6T.

wis a weight vector that parameterizes the classifier, denotes the matrix transpose.

We trained a Linear Regression classifier to learn the weight vector W = (7.01, w2, 2123, 2124) that would combine the above feature.

It was calculated as dot product between the learned weight vector W and the feature vector for answer \II“.

In order to learn the weight vector V that would combine the above scores, we asked three human annotators to generate question-biased extractive summaries based on all answers available for a certain question.

In the training process of HL-flat, the algorithm reflexes the restriction in the HL-SOT algorithm that requires the weight vector wig; of the classifier i is only updated on the examples that are positive for its parent node.

Defining the f function Let wl, ..., 212 N be weight vectors that define linear-threshold classifiers ofeach node in SOT.

The Formula 1 restricts that the weight vector wig; of the classifier i is only updated on the examples that are positive for its parent node.