/// <summary>
/// Parses the specified words, order is by default 2
/// </summary>
/// <param name="words">The words.</param>
/// <param name="posTags">The pos tags.</param>
/// <param name="modelName">Name of the model.</param>
/// <param name="labeled">if set to <c>true</c> [labeled].</param>
/// <param name="labels">The labels.</param>
/// <param name="deps">The deps.</param>
public static void Parse(string[] words, string[] posTags, string modelName, bool labeled, out string[] labels, out int[] deps)
{
DependencyPipe pipe = new DependencyPipe2O(true);
pipe.setLabel(labeled);
var dp = new DependencyParser(pipe);
dp.LoadModel(modelName);
pipe.CloseAlphabets();
dp.OutputParses(words, posTags, out labels, out deps);
}
/// <summary>
/// Train the paser with train file.
/// </summary>
/// <param name="trainFile">Training file path</param>
/// <param name="modelName">Model Path</param>
/// <param name="numOfTrainingIterations">Number of iteration for training feature weights; Default=10</param>
/// <param name="isProjective">Type of Parsing; Default is True; Free order languages need to be nonprojective
/// True: use the projective parsing algorithm during training i.e. The Eisner algorithm
/// False: use the non-projective parsing algorithm during training i.e. The Chu-Liu-Edmonds algorithm</param>
/// <param name="trainingK"> Specifies the k-best parse set size to create constraints during training
/// Default is 1
/// For non-projective parsing algorithm, k-best decoding is approximate</param>
/// <param name="createForest">Default is "true"
/// If create-forest is false, it will not create the training parse forest . It assumes it has been created.
/// This flag is useful if you are training many models on the same data and
/// features but using different Parameters (e.g. training iters, decoding type).</param>
/// <param name="order">is either 1 or 2
/// Default is 1
/// Specifies the order/scope of features. 1 only has features over single edges
/// and 2 has features over pairs of adjacent edges in the tree</param>
public static void Train(string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK, bool createForest, int order)
{
DependencyParser.SecondOrder = (order == 2 ? true : false);
DependencyParser.DecodeType = isProjective ? ProjectiveTypes.Projective : ProjectiveTypes.NonProjective;
DependencyPipe pipe =
order == 2 ? new DependencyPipe2O(createForest) : new DependencyPipe(createForest);
DependencyParser.SecondOrder = order == 2 ? true : false;
pipe.SetLabeled(trainFile);
string trainForest = trainFile + ".forest";
DependencyInstance[] trainingData = pipe.CreateInstances(trainFile, trainForest);
pipe.CloseAlphabets();
var dp = new DependencyParser(pipe);
dp.Train(trainingData, trainFile, trainForest);
dp.SaveModel(modelName);
}
/// <summary>
/// Parses the specified words with the specified trained parser.
/// </summary>
/// <param name="words">The words.</param>
/// <param name="posTags">The pos tags.</param>
/// <param name="parser">The trained parser.</param>
/// <param name="labels">The labels.</param>
/// <param name="deps">The deps.</param>
public static void Parse(string[] words, string[] posTags, DependencyParser parser, out string[] labels, out int[] deps)
{
parser.OutputParses(words, posTags, out labels, out deps);
}
/// <summary>
/// Tests the specified test file.
/// </summary>
/// <param name="testFile">The test file.</param>
/// <param name="modelName">Model Path</param>
/// <param name="outFile">The parser output path</param>
/// <param name="order">is either 1 or 2
/// Default is 1
/// Specifies the order/scope of features. 1 only has features over single edges
/// and 2 has features over pairs of adjacent edges in the tree</param>
public static void Test(string testFile, string modelName, string outFile, int order)
{
DependencyParser.SecondOrder = (order == 2 ? true : false);
DependencyPipe pipe =
order == 2 ? new DependencyPipe2O(true) : new DependencyPipe(true);
pipe.SetLabeled(testFile);
var dp = new DependencyParser(pipe);
dp.LoadModel(modelName);
pipe.CloseAlphabets();
dp.OutputParses(testFile, outFile);
}
