alpha in equation and how to select parameters in training type classifier about deeptype HOT 1 OPEN

Training is certainly stochastic / dependent on seed so I think you'll have some variation on the exact probabilities you obtain. The alpha term is obtained by fitting it using the output of the model on a heldout set, e.g.:

$$\alpha^* = \text{argmax}{\alpha} = \sum{i}^n \mathrm{Prob}(\mathrm{label}_i, \mathrm{TypeProbs}(\mathrm{sent}_i, \alpha))$$

Where alpha is the weight given for the other class. I don't think it matters how you solve for alpha, you can use gradient descent, or np.linalg.solve since it's a linear term in the equation above. (perhaps a smarter solution would be to predict alpha based on the sentence so that you can use a context-specific alpha).

from deeptype.