/// <summary>
/// Method for collecting candidates for replacing target words (at the sentence level). Candidates are retrieved as most similar words obtained via word embeddings (cosine similarity between embedding vectors)
/// </summary>
/// <param name="sentence">Sentence to be simplified</param>
/// <param name="substCandidates">List of all substitution candidates</param>
/// <param name="icTreshold">The information content treshold</param>
/// <param name="word">Target word to be replaced (if not provided, all content words are considered for replacing)</param>
public void CollectCandidates(SentenceAnnotation sentence, List <Tuple <TokenAnnotation, List <Tuple <string, double> > > > substCandidates, double icTreshold, string word = null)
{
List <string> sides = new List <string> {
"north", "west", "south", "east"
};
var contentTokens = sentence.Tokens.Where(t => t.IsContent() && !t.POSTag.StartsWith("C") && string.IsNullOrEmpty(t.NamedEntity) && !sides.Any(si => t.Text.ToLower().Contains(si))).ToList();
contentTokens.ForEach(ct => {
var ic = InformationContent.GetRelativeInformationContent(string.IsNullOrEmpty(ct.Lemma) ? ct.Text.ToLower() : ct.Lemma.ToLower());
if (ic > icTreshold && (!char.IsUpper(ct.Text[0]) || sentence.Tokens.IndexOf(ct) == 0))
{
if (word == null || ct.Text.ToLower() == word.ToLower())
{
var candidates = new List <Tuple <string, double> >();
var cands1 = VectorSpace.GetMostSimilar(ct.Text.ToLower(), 30);
if (cands1 != null)
{
candidates.AddRange(cands1);
}
if (!string.IsNullOrEmpty(ct.Lemma))
{
var cands2 = VectorSpace.GetMostSimilar(ct.Lemma.ToLower(), 30);
if (cands2 != null)
{
candidates.AddRange(cands2);
}
}
candidates = candidates.OrderByDescending(x => x.Item2).ToList();
substCandidates.Add(new Tuple <TokenAnnotation, List <Tuple <string, double> > >(ct, candidates));
}
}
});
}
/// <summary>
/// Creates a new instance of Manipulator
/// </summary>
/// <param name="graphics">The IGraphicsDeviceService with which drawing will be performed</param>
/// <param name="camera">A provider for camera view and projection data</param>
/// <param name="input">A provider for input data</param>
public Manipulator(IGraphicsDeviceService graphics, XICamera camera, XIInputProvider input)
{
mGraphics = graphics;
mCamera = camera;
mInput = input;
mSelectedAxes = AxisFlags.None;
mActiveMode = TransformationMode.None;
mEnabledModes = TransformationMode.None;
mVectorSpace = VectorSpace.World;
mPickBuffer = new XPickBuffer(graphics);
mSettings = new ManipulatorSettings();
mSettings.RestoreDefaults();
mGraphics.DeviceCreated += new EventHandler(OnDeviceCreated);
mGraphics.DeviceReset += new EventHandler(CreateDepthBuffer);
mGraphics.DeviceResetting += new EventHandler(DisposeDepthBuffer);
mGraphics.DeviceDisposing += new EventHandler(OnDeviceDisposing);
if ((mGraphics.GraphicsDevice != null) && !mGraphics.GraphicsDevice.IsDisposed)
{
OnDeviceCreated(this, null);
CreateDepthBuffer(this, null);
}
mUndoStack = new Stack <TransformState>();
mRedoStack = new Stack <TransformState>();
mDrawFunctions = new DrawFunctions();
mManipFunctions = new ManipFunctions();
mDrawFunctions[TransformationMode.None][AxisFlags.X]
= mDrawFunctions[TransformationMode.None][AxisFlags.Y]
= mDrawFunctions[TransformationMode.None][AxisFlags.Z]
= delegate(AxisFlags axis)
{
Vector3 unit = GetUnitAxis(axis);
XPrimitives.DrawLine(mGraphics.GraphicsDevice, Vector3.Zero, unit);
};
InitTranslation();
InitRotation();
InitScale();
}
public void Poke(ushort address, byte value)
{
// Regular RAM
if (address < 0xFC00)
{
Ram.Poke(address, value);
return;
}
// "These overlays are controlled by the bits in the hardware register at FFF9."
if (address == 0xFFF9) // Special case for memory map control register
{
ConfigureMemoryMapControl(value);
return;
}
// "FFFE, FFFF CPU Interrupt Vector (RAM or ROM)
// FFFC, FFFD CPU Reset Vector (RAM or ROM)
// FFFA, FFFB CPU NMI Vector (RAM or ROM)"
if (address > 0xFFFA)
{
VectorSpace.Poke(address, value); return;
}
// "FE00 thru FFF7 ROM Space"
if (address >= 0xFE00)
{
RomSpace.Poke(address, value); return;
}
// "FD00 thru FDFF Mikey Space"
if (address >= 0xFD00)
{
MikeySpace.Poke(address, value); return;
}
// "FC00 thru FCFF Suzy Space"
if (address >= 0xFC00)
{
SuzySpace.Poke(address, value); return;
}
}
/// <summary>
/// Deploys the specified settings.
/// </summary>
/// <param name="settings">The settings.</param>
/// <param name="vectorSpace">The vector space.</param>
public void Deploy(FeatureVectorConstructorSettings settings, VectorSpace vectorSpace)
{
// dimensionFunctionSet = new List<FeatureSpaceDimensionBase>();
// creates instances of dimension value providers - for each label in the vector space
foreach (dimensionSpecification ld in settings.labelDimensions)
{
foreach (VectorLabel label in vectorSpace.labels)
{
FeatureSpaceDimensionSimilarity dimensionInstance = new FeatureSpaceDimensionSimilarity();
IVectorSimilarityFunction functionInstance = (IVectorSimilarityFunction)TypeProviders.similarityFunctions.GetInstance(ld.functionName);
IVector classVector = vectorSpace.labels.First(x => x.name == label.name);
dimensionInstance.similarityFunction = functionInstance;
dimensionInstance.classVector = classVector;
dimensionFunctionSet.Add(dimensionInstance);
}
}
}
public byte Peek(ushort address)
{
// Regular RAM
if (address < 0xFC00)
{
return(Ram.Peek(address));
//return directAccessRam[address];
}
// "These overlays are controlled by the bits in the hardware register at FFF9."
if (address == 0xFFF9) // Special case for memory map control register
{
// "Both Mikey and Suzy accept a write at those addresses but only Mikey responds to a read."
// Since we will be passing regular RAM memory to Suzy, it is OK to always return value
// because only Mikey will be going through this MMU.
return(MAPCTL.ByteData);
}
// For details on address ranges see Poke() implementation
if (address >= 0xFFFA)
{
return(VectorSpace.Peek(address));
}
if (address >= 0xFE00)
{
return(RomSpace.Peek(address));
}
if (address >= 0xFD00)
{
return(MikeySpace.Peek(address));
}
if (address >= 0xFC00)
{
return(SuzySpace.Peek(address));
}
return(0);
}
/// <summary>
/// Oredring the candidates for the ranking task, when the candidates are given. The ordering is somewhat different
/// </summary>
/// <param name="document">The document whose text is to be simplified</param>
/// <param name="substitutionCandidates">Substitution candidates</param>
/// <param name="target">Target word</param>
/// <param name="contextSize">The size of the context of the target word to be compared semantically with candidate replacements</param>
/// <returns></returns>
public List <string> OrderGivenSubstitutionCandidates(Document document, List <string> substitutionCandidates, string target, int contextSize)
{
EngMorphology morph = new EngMorphology();
List <Tuple <TokenAnnotation, string> > substitutions = new List <Tuple <TokenAnnotation, string> >();
//List<string> metrics = new List<string> { "sim", "ic-diff", "context-sim", "length", "lm-bigram-pre", "lm-bigram-post", "lm-trigram-pre", "lm-trigram-post" };
List <string> metrics = new List <string> {
"context-sim" /*, "lm-bigram-pre", "lm-bigram-post"*/, "ic-diff" /*, "lm-trigram-pre", "lm-trigram-post"*/
};
//List<string> metrics = new List<string> { "ic-diff" };
Dictionary <string, string> candidateChanges = new Dictionary <string, string>();
var targetToken = document.AllTokens.Where(t => t.Text == target).Last();
var preceedingSentencePart = document.Text.Substring(0, targetToken.StartPositionSentence);
var followingSentencePart = document.Text.Substring(targetToken.StartPositionSentence + targetToken.Text.Length);
var targetLemmaIC = InformationContent.GetRelativeInformationContent(targetToken.Lemma.ToLower());
var targetWordIC = InformationContent.GetRelativeInformationContent(targetToken.Text.ToLower());
var targetContextTokens = document.AllTokens.Where(t => Math.Abs(document.AllTokens.IndexOf(t) - targetToken.SentenceIndex) > 0 && Math.Abs(document.AllTokens.IndexOf(t) - targetToken.SentenceIndex) <= contextSize && t.IsContent()).ToList();
Dictionary <string, Dictionary <string, double> > scores = new Dictionary <string, Dictionary <string, double> >();
substitutionCandidates.ForEach(candidate => {
var candidateText = candidate;
if (candidateText.Contains(","))
{
var splitCand = candidateText.Split(new char[] { ',' }, StringSplitOptions.RemoveEmptyEntries).ToList();
string selected = string.Empty;
double maxIC = double.MinValue;
splitCand.ForEach(sc =>
{
var scic = InformationContent.GetRelativeInformationContent(sc.Trim().ToLower());
if (scic > maxIC)
{
selected = sc.Trim();
maxIC = scic;
}
});
candidateText = selected;
}
if (candidateText.Trim().Contains(" "))
{
var tokens = (new EngPOSTagger()).Annotate(candidateText.Trim()).ToList();
var contentTokens = tokens.Where(x => ((TokenAnnotation)x).IsContent()).ToList();
var change = contentTokens.Count > 0 ? ((TokenAnnotation)(contentTokens.Last())).Text.Trim() : ((TokenAnnotation)(tokens.First())).Text.Trim();
candidateText = change;
}
var candidateLemmaIC = InformationContent.GetRelativeInformationContent(candidateText.ToLower());
var candidateWordIC = !string.IsNullOrEmpty(candidateText) ? InformationContent.GetRelativeInformationContent(candidateText.ToLower()) : 1;
var candidateIC = candidateWordIC == 1 ? candidateLemmaIC : candidateWordIC;
var targetIC = targetWordIC == 1 ? targetLemmaIC : targetWordIC;
var candidateContextSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), candidateText.ToLower())).Where(x => x >= -1).ToList();
var candidateContextSimilarity = candidateContextSimilarities.Count > 0 ? candidateContextSimilarities.Average() : 0;
scores.Add(candidate, new Dictionary <string, double>());
var sim = VectorSpace.Similarity(targetToken.Text.ToLower().Trim(), candidateText.ToLower().Trim());
if (sim < 1)
{
scores[candidate].Add("sim", sim);
}
scores[candidate].Add("ic-diff", candidateIC);
scores[candidate].Add("context-sim", candidateContextSimilarity);
scores[candidate].Add("length", candidateText.Length);
var tokenIndex = document.AllTokens.IndexOf(targetToken);
// bigram LM
if (tokenIndex > 0)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(document.AllTokens[document.AllTokens.IndexOf(targetToken) - 1].Text.ToLower(), candidateText);
scores[candidate].Add("lm-bigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-bigram-pre", 0);
}
if (tokenIndex < document.AllTokens.Count - 1)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(candidateText, document.AllTokens[document.AllTokens.IndexOf(targetToken) + 1].Text.ToLower());
scores[candidate].Add("lm-bigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-bigram-pre", 0);
}
// trigram LM
if (tokenIndex > 1)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(document.AllTokens[document.AllTokens.IndexOf(targetToken) - 2].Text.ToLower(), document.AllTokens[document.AllTokens.IndexOf(targetToken) - 1].Text.ToLower(), candidateText);
scores[candidate].Add("lm-trigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-trigram-pre", 0);
}
if (tokenIndex < document.AllTokens.Count - 2)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(candidateText, document.AllTokens[document.AllTokens.IndexOf(targetToken) + 1].Text.ToLower(), document.AllTokens[document.AllTokens.IndexOf(targetToken) + 2].Text.ToLower());
scores[candidate].Add("lm-trigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidate].Add("lm-trigram-post", 0);
}
});
LastSubstitutionCandidates = new List <Tuple <TokenAnnotation, List <string> > >();
LastSubstitutionCandidates.Add(new Tuple <TokenAnnotation, List <string> >(targetToken, scores.Select(x => x.Key).ToList()));
var allRanks = new List <Dictionary <string, int> >();
metrics.ForEach(m =>
{
var featDict = scores.Where(x => x.Value.ContainsKey(m)).ToDictionary(x => x.Key, x => x.Value[m]);
allRanks.Add(TrainingExample.RankExamplesByNumericFeature(featDict, m == "length" || m == "ic-diff"));
});
var allCandidates = scores.Select(x => x.Key).ToList();
Dictionary <string, double> averageRankings = allCandidates.ToDictionary(x => x, x => allRanks.Where(y => y.ContainsKey(x)).Select(r => r[x]).Average());
return(averageRankings.OrderBy(r => r.Value).Select(x => x.Key).ToList());
}
/// <summary>
/// Choosing the substitutions for target words. This means we rank the candidates according to several features (similarity with target word, information content reduction, similarity with context words, ...)
/// </summary>
/// <param name="document">The document whose text is to be simplified</param>
/// <param name="substCandidates">All the pairs of target words and collected candidate replacements</param>
/// <param name="contextSize">The size of the context of the target word to be considered for measuring the similarity between candidate words and target word context</param>
/// <param name="noSubstitutionWords">Stopwords, never to be considered for simplification</param>
/// <param name="similarityTreshold">The treshold for semantic similarity between target word and candidate replacement</param>
/// <param name="icReplacementCandidateTreshold">Information content treshold for replacing the target word</param>
/// <param name="word"></param>
/// <returns>The list of substitutions (tuple of target token and candidate replacement word)</returns>
public List <Tuple <TokenAnnotation, string> > GetSubstitutions(Document document, List <Tuple <TokenAnnotation, List <Tuple <string, double> > > > substCandidates, int contextSize, List <string> noSubstitutionWords, double similarityTreshold, double icReplacementCandidateTreshold, string word = null)
{
EngMorphology morph = new EngMorphology();
List <Tuple <TokenAnnotation, string> > substitutions = new List <Tuple <TokenAnnotation, string> >();
List <string> metrics = new List <string> {
"sim", "ic-diff", "context-sim", "lm-bigram-pre", "lm-bigram-post", "lm-trigram-pre", "lm-trigram-post"
};
//List<string> metrics = new List<string> { "sim", "lm-bigram-pre", "lm-bigram-post", /*"ic-diff", "lm-trigram-pre", "lm-trigram-post"*/ };
substCandidates.ForEach(sc => {
if (!noSubstitutionWords.Contains(sc.Item1.Text.ToLower()))
{
if (word == null || sc.Item1.Text.ToLower() == word)
{
Dictionary <string, Dictionary <string, double> > scores = new Dictionary <string, Dictionary <string, double> >();
var targetToken = sc.Item1;
var sentence = document.Sentences.Where(s => s.Tokens.Any(t => t.StartPosition == targetToken.StartPosition && t.Text == targetToken.Text)).Single();
var targetTokenCopy = sentence.Tokens.Where(t => t.StartPosition == targetToken.StartPosition && t.Text == targetToken.Text).Single();
var preceedingSentencePart = sentence.Text.Substring(0, targetTokenCopy.StartPositionSentence);
var followingSentencePart = sentence.Text.Substring(targetTokenCopy.StartPositionSentence + targetTokenCopy.Text.Length);
var targetLemmaIC = InformationContent.GetRelativeInformationContent(targetToken.Lemma.ToLower());
var targetWordIC = InformationContent.GetRelativeInformationContent(targetToken.Text.ToLower());
var targetContextTokens = sentence.Tokens.Where(t => Math.Abs(sentence.Tokens.IndexOf(t) - targetTokenCopy.SentenceIndex) > 0 && Math.Abs(sentence.Tokens.IndexOf(t) - targetTokenCopy.SentenceIndex) <= contextSize && t.IsContent()).ToList();
var targetCtxtSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), targetToken.Lemma.ToLower())).Where(x => x >= -1).ToList();
var targetContextSimilarity = targetCtxtSimilarities.Count > 0 ? targetCtxtSimilarities.Average() : 0;
if (sc.Item2 != null)
{
sc.Item2.ForEach(candidate =>
{
try
{
var candidateLemmaIC = InformationContent.GetRelativeInformationContent(candidate.Item1.ToLower());
string key = candidate.Item1 + "<->" + targetToken.POSTag;
//var candidateInPOS = EngMorphology.GetForm(candidate.Item1, targetToken.POSTag);
//if (!CandidateInPoSLookup.ContainsKey(key)) CandidateInPoSLookup.Add(key, candidateInPOS);
var candidateInPOS = CandidateInPoSLookup.ContainsKey(key) ? CandidateInPoSLookup[key] : candidate.Item1;
var candidateWordIC = !string.IsNullOrEmpty(candidateInPOS) ? InformationContent.GetRelativeInformationContent(candidateInPOS.ToLower()) : 1;
var candidateIC = candidateWordIC == 1 ? candidateLemmaIC : candidateWordIC;
var targetIC = targetWordIC == 1 ? targetLemmaIC : targetWordIC;
if (!string.IsNullOrEmpty(candidateInPOS) && targetLemmaIC > icReplacementCandidateTreshold && (candidateIC < targetIC /*|| Math.Abs(targetIC - candidateIC) < 0.05*/))
{
var artificialSentence = preceedingSentencePart + candidateInPOS + followingSentencePart;
var artTokens = (new EngPOSTagger()).Annotate(artificialSentence).Select(x => (TokenAnnotation)x).ToList();
morph.AnnotateMorphology(artTokens);
var candidateToken = artTokens.Where(x => x.StartPositionSentence == targetTokenCopy.StartPositionSentence /*&& x.Text == candidateInPOS*/).Single();
var candidateContextSimilarities = targetContextTokens.Select(x => VectorSpace.Similarity(x.Lemma.ToLower(), candidateToken.Lemma.ToLower())).Where(x => x >= -1).ToList();
var candidateContextSimilarity = candidateContextSimilarities.Count > 0 ? candidateContextSimilarities.Average() : targetContextSimilarity;
// POS-tag compatibility is a second prerequisite
bool sameWord = candidate.Item1.Contains(targetToken.Text) || targetToken.Text.Contains(candidate.Item1) ||
candidate.Item1.Contains(targetToken.Lemma) || targetToken.Lemma.Contains(candidate.Item1) ||
candidateInPOS.Contains(targetToken.Text) || targetToken.Text.Contains(candidateInPOS) ||
candidateInPOS.Contains(targetToken.Lemma) || targetToken.Lemma.Contains(candidateInPOS);
bool sameAsContext = targetContextTokens.Any(ct =>
candidate.Item1.Contains(ct.Text) || ct.Text.Contains(candidate.Item1) ||
candidate.Item1.Contains(ct.Lemma) || ct.Lemma.Contains(candidate.Item1) ||
candidateInPOS.Contains(ct.Text) || ct.Text.Contains(candidateInPOS) ||
candidateInPOS.Contains(ct.Lemma) || ct.Lemma.Contains(candidateInPOS));
if (candidate.Item2 >= similarityTreshold && (candidateToken.POSTag == targetToken.POSTag) && !sameWord && !sameAsContext)
{
if (!scores.ContainsKey(candidateInPOS))
{
scores.Add(candidateInPOS, new Dictionary <string, double>());
scores[candidateInPOS].Add("sim", candidate.Item2);
scores[candidateInPOS].Add("ic-diff", targetIC - candidateIC);
scores[candidateInPOS].Add("context-sim", candidateContextSimilarity);
scores[candidateInPOS].Add("length", candidateInPOS.Length);
var tokenIndex = sentence.Tokens.IndexOf(targetTokenCopy);
// bigram LM
if (tokenIndex > 0)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 1].Text.ToLower(), candidateInPOS);
scores[candidateInPOS].Add("lm-bigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-bigram-pre", 0);
}
if (tokenIndex < sentence.Tokens.Count - 1)
{
var lmScore = EnglishLanguageModel.Instance.GetBigramLMScore(candidateInPOS, sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 1].Text.ToLower());
scores[candidateInPOS].Add("lm-bigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-bigram-post", 0);
}
// trigram LM
if (tokenIndex > 1)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 2].Text.ToLower(), sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) - 1].Text.ToLower(), candidateInPOS);
scores[candidateInPOS].Add("lm-trigram-pre", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-trigram-pre", 0);
}
if (tokenIndex < sentence.Tokens.Count - 2)
{
var lmScore = EnglishLanguageModel.Instance.GetTrigramLMScore(candidateInPOS, sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 1].Text.ToLower(), sentence.Tokens[sentence.Tokens.IndexOf(targetTokenCopy) + 2].Text.ToLower());
scores[candidateInPOS].Add("lm-trigram-post", lmScore.HasValue ? lmScore.Value : -100);
}
else
{
scores[candidateInPOS].Add("lm-trigram-post", 0);
}
}
}
}
}
catch { }
});
}
LastSubstitutionCandidates = new List <Tuple <TokenAnnotation, List <string> > >();
LastSubstitutionCandidates.Add(new Tuple <TokenAnnotation, List <string> >(targetToken, scores.Select(x => x.Key).ToList()));
if (scores.Count > 0)
{
var allRanks = new List <Dictionary <string, int> >();
metrics.ForEach(m => {
var featDict = scores.ToDictionary(x => x.Key, x => x.Value[m]);
allRanks.Add(TrainingExample.RankExamplesByNumericFeature(featDict, m == "length"));
});
var allCandidates = scores.Select(x => x.Key).ToList();
Dictionary <string, double> averageRankings = allCandidates.ToDictionary(x => x, x => allRanks.Select(r => r[x]).Average());
var finalRanking = averageRankings.OrderBy(r => r.Value).ToList();
double topScore = finalRanking[0].Value;
var equal = new List <string>();
finalRanking.ForEach(fr => {
if (fr.Value == topScore)
{
equal.Add(fr.Key);
}
});
var finalChoice = equal.Where(eq => equal.Where(eq2 => eq2 != eq).All(eq2 => scores[eq]["sim"] >= scores[eq2]["sim"])).First();
substitutions.Add(new Tuple <TokenAnnotation, string>(targetToken, finalChoice));
}
}
}
});
return(substitutions);
}
