public TrigramWithLinearInterpolationLanguageModel(double l1, double l2, double l3, ISmoother smoother)
{
UnigramLM = new UnigramLanguageModel {
Smoother = smoother
};
BigramLM = new BigramLanguageModel {
Smoother = smoother
};
TrigramLM = new TrigramLanguageModel {
Smoother = smoother
};
Smoother = smoother;
L1 = l1;
L2 = l2;
L3 = l3;
}
public static LanguageModelHyperparameters GenerateFromArguments(string args)
{
args = args.ToLower();
var splittedArgs = args.Split(' ', StringSplitOptions.RemoveEmptyEntries);
// Smoothers
// Create the collection-level unigram model with no smoothing (max-likelihood) used in some smoothing techniques
INGramLanguageModel collectionLevelLanguageModel = new UnigramLanguageModel {
Smoother = new MaxLikelihoodSmoother()
};
ISmoother smoother = null;
switch (splittedArgs[Array.IndexOf(splittedArgs, "-smoothingtechnique") + 1])
{
case "ml":
smoother = new MaxLikelihoodSmoother();
break;
case "addk":
smoother = new AddKSmoother {
K = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1])
};
break;
case "jm":
smoother = new JelinekMercerSmoother {
CollectionLevelLanguageModel = collectionLevelLanguageModel, L = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1])
};
break;
case "dirichlet":
smoother = new DirichletSmoother {
CollectionLevelLanguageModel = collectionLevelLanguageModel, M = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1])
};
break;
case "ad":
smoother = new AbsoluteDiscountSmoother {
CollectionLevelLanguageModel = collectionLevelLanguageModel, D = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1])
};
break;
case "ts":
smoother = new TwoStageSmoother {
CollectionLevelLanguageModel = collectionLevelLanguageModel, L = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1]), M = Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l2") + 1])
};
break;
}
// LM
var nGramLanguageModels = Corpus.CategoriesMap.ToDictionary(cdp => cdp.Key, cdp =>
{
INGramLanguageModel modelToUse = null;
switch (splittedArgs[Array.IndexOf(splittedArgs, "-lm") + 1])
{
case "unigram":
modelToUse = new UnigramLanguageModel {
Smoother = smoother
};
break;
case "bigram":
modelToUse = new BigramLanguageModel {
Smoother = smoother
};
break;
case "trigram":
modelToUse = new TrigramLanguageModel {
Smoother = smoother
};
break;
case "trigramwithlinearinterpolation":
modelToUse = new TrigramWithLinearInterpolationLanguageModel(
Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1]),
Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l2") + 1]),
Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l3") + 1]),
smoother);
break;
}
return(modelToUse);
});
return(new LanguageModelHyperparameters
{
CategoryNGramLanguageModelsMap = nGramLanguageModels,
UnkRatio = Array.IndexOf(splittedArgs, "-unkratio") >= 0 ? Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-unkratio") + 1]) : 0.1,
IgnoreCase = Array.IndexOf(splittedArgs, "-ignorecase") >= 0,
L1 = Array.IndexOf(splittedArgs, "-l1") >= 0 ? Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l1") + 1]) : 0.0,
L2 = Array.IndexOf(splittedArgs, "-l2") >= 0 ? Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l2") + 1]) : 0.0,
L3 = Array.IndexOf(splittedArgs, "-l3") >= 0 ? Double.Parse(splittedArgs[Array.IndexOf(splittedArgs, "-l3") + 1]) : 0.0,
CollectionLevelLanguageModel = collectionLevelLanguageModel,
});
}