/// <summary>
/// Generates chunk tags for the given sequence returning the result in an array.
/// </summary>
/// <param name="tokens">an array of the tokens or words of the sequence.</param>
/// <param name="tags">an array of the pos tags of the sequence.</param>
/// <returns>An array of chunk tags for each token in the sequence or a <c>null</c> value if none.</returns>
/// <exception cref="System.ArgumentNullException">
/// The <paramref name="tokens"/> is null.
/// or
/// The <paramref name="tags"/> is null.
/// </exception>
/// <exception cref="System.ArgumentOutOfRangeException">The token array is empty.</exception>
public string[] Chunk(string[] tokens, string[] tags)
{
if (tokens == null)
{
throw new ArgumentNullException("tokens");
}
if (tokens.Length == 0)
{
throw new ArgumentOutOfRangeException("tokens", "The token array is empty.");
}
if (tags == null)
{
throw new ArgumentNullException("tags");
}
bestSequence = model.BestSequence(tokens, new object[] { tags }, contextGenerator, sequenceValidator);
return(bestSequence == null ? null : bestSequence.Outcomes.ToArray());
}
/// <summary>
/// Returns the lemma of the specified word with the specified part-of-speech.
/// </summary>
/// <param name="tokens">An array of the tokens.</param>
/// <param name="tags">An array of the POS tags.</param>
/// <returns>An array of lemma classes for each token in the sequence.</returns>
/// <exception cref="ArgumentNullException"><paramref name="tokens" /> or <paramref name="tags" /></exception>
/// <exception cref="ArgumentException">The arguments must have the same length.</exception>
public string[] Lemmatize(string[] tokens, string[] tags)
{
if (tokens == null)
{
throw new ArgumentNullException(nameof(tokens));
}
if (tags == null)
{
throw new ArgumentNullException(nameof(tags));
}
if (tokens.Length != tags.Length)
{
throw new ArgumentException("The arguments must have the same length.");
}
bestSequence = model.BestSequence(tokens, new object[] { tags }, contextGenerator, sequenceValidator);
return(bestSequence.Outcomes.ToArray());
}
/// <summary>
/// Generates name tags for the given sequence, typically a sentence, returning token spans for any identified names.
/// </summary>
/// <param name="tokens">An array of the tokens or words of the sequence, typically a sentence.</param>
/// <param name="additionalContext">Features which are based on context outside of the sentence but which should also be used.</param>
/// <returns>An array of spans for each of the names identified.</returns>
public Span[] Find(string[] tokens, string[][] additionalContext)
{
additionalContextFeatureGenerator.SetCurrentContext(additionalContext);
bestSequence = model.BestSequence(tokens,
Array.ConvertAll(additionalContext, input => (object)input),
contextGenerator,
sequenceValidator);
var outcomes = bestSequence.Outcomes.ToArray();
contextGenerator.UpdateAdaptiveData(tokens, outcomes);
var spans = sequenceCodec.Decode(outcomes);
var probs = Probs(spans);
for (var i = 0; i < probs.Length; i++)
{
spans[i].Probability = probs[i];
}
return(spans);
}
/// <summary>
/// Generates chunk tags for the given sequence returning the result in an array.
/// </summary>
/// <param name="tokens">an array of the tokens or words of the sequence.</param>
/// <param name="tags">an array of the pos tags of the sequence.</param>
/// <returns>an array of chunk tags for each token in the sequence.</returns>
public string[] Chunk(string[] tokens, string[] tags)
{
bestSequence = model.BestSequence(tokens, new object[] { tags }, contextGenerator, sequenceValidator);
return(bestSequence.Outcomes.ToArray());
}
/// <summary>
/// Assigns the sentence of tokens pos tags.
/// </summary>
/// <param name="sentence">The sentence of tokens to be tagged.</param>
/// <param name="additionalContext">Any addition context specific to a class implementing this interface.</param>
/// <returns>an array of pos tags for each token provided in sentence.</returns>
public string[] Tag(string[] sentence, object[] additionalContext)
{
bestSequence = model.BestSequence(sentence, additionalContext, ContextGenerator, SequenceValidator);
return(bestSequence.Outcomes.ToArray());
}