/// <summary>
/// Return the similarity of two given words with a taxonomy.
/// </summary>
/// <param name="word1"></param>
/// <param name="word2"></param>
/// <param name="strategy"></param>
/// <returns></returns>
float GetSimilarity(HierarchicalWordData word1, HierarchicalWordData word2, int strategy)
{
if (word1.WordInfo.Pos != word2.WordInfo.Pos || word1.WordInfo.Pos == PartsOfSpeech.Unknown) return 0.0F;
if (word1.WordInfo.Word == word2.WordInfo.Word) return 1.0F;
int pathLength, lcaDepth, depth_1, depth_2;
FindLeastCommonAncestor(new HierarchicalWordData[2] { word1, word2 }, out pathLength, out lcaDepth, out depth_1, out depth_2);
if (pathLength == int.MaxValue) return 0.0F;
float sim=0.0F;
if (strategy == 1)//Path Length
{
if (pathLength == 0) return 1.0F;
else
sim=1.0F / (float)pathLength;
}
else
if (strategy == 2) //Wu & Palmer
{
if (pathLength == 0) return 1.0F;
else
sim=(float)(lcaDepth) / (float)(depth_1 + depth_2);
}
return (float)Math.Round(sim, 2);
}
//MyWordInfo[] _myWordsInfo_i, _myWordsInfo_j;
//private void MyInitOld()
//{
// _myWordsInfo1 = Disambiguate(_source);
// _myWordsInfo2 = Disambiguate(_target);
// m = _myWordsInfo1.Length; n = _myWordsInfo2.Length;
// _similarity =new float[m, n] ;
// for (int i=0; i < m; i++)
// {
// _myWordsInfo1[i].Sense = _myWordsInfo1[i].Sense < 0 ? 0 : _myWordsInfo1[i].Sense;
// string word1 = _source[i];
// for (int j=0; j < n; j++)
// {
// _myWordsInfo2[i].Sense = _myWordsInfo2[i].Sense < 0 ? 0 : _myWordsInfo2[i].Sense;
// string word2=_target[j];
// WordDistance distance = new WordDistance();
// float weight = distance.GetSimilarity(_myWordsInfo1[i], _myWordsInfo2[j]);
// _similarity[i, j]=weight;
// }
// }
//}
float[][] GetSimilarityMatrix(string[] string1, string[] string2)
{
m = string1.Length; n = string2.Length;
float[][] simMatrix = new float[m][];
Wnlib.PartsOfSpeech[] POSEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
HierarchicalWordData[][] wordData_1 = new HierarchicalWordData[m][];
HierarchicalWordData[][] wordData_2 = new HierarchicalWordData[n][];
for (int i = 0; i < m; i++)
{
simMatrix[i] = new float[n];
}
for (int i = 0; i < m; i++)
{
wordData_1[i] = new HierarchicalWordData[POSEnum.Length];
}
for (int j = 0; j < n; j++)
{
wordData_2[j] = new HierarchicalWordData[POSEnum.Length];
}
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
float synDist = AcronymChecker.GetEditDistanceSimilarity(string1[i], string2[j]);
for (int partOfSpeech = 1; partOfSpeech < POSEnum.Length; partOfSpeech++)
{
if (wordData_1[i][partOfSpeech] == null)
{
MyWordInfo myWordsInfo_i = new MyWordInfo(string1[i], POSEnum[partOfSpeech]);
wordData_1[i][partOfSpeech] = new HierarchicalWordData(myWordsInfo_i);
}
if (wordData_2[j][partOfSpeech] == null)
{
MyWordInfo myWordsInfo_j = new MyWordInfo(string2[j], POSEnum[partOfSpeech]);
wordData_2[j][partOfSpeech] = new HierarchicalWordData(myWordsInfo_j);
}
WordSimilarity wordDistance = new WordSimilarity();
float semDist = wordDistance.GetSimilarity(wordData_1[i][partOfSpeech], wordData_2[j][partOfSpeech]);
float weight = Math.Max(synDist, semDist);
if (simMatrix[i][j] < weight)
{
simMatrix[i][j] = weight;
}
}
}
}
return(simMatrix);
}
public float GetSimilarity(string word1, string word2)
{
Wnlib.PartsOfSpeech[] POSEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
float minSim = float.MaxValue;
for (int partOfSpeech = 1; partOfSpeech < POSEnum.Length; partOfSpeech++)
{
HierarchicalWordData data_1 = new HierarchicalWordData(new MyWordInfo(word1, POSEnum[partOfSpeech]));
HierarchicalWordData data_2 = new HierarchicalWordData(new MyWordInfo(word2, POSEnum[partOfSpeech]));
float sim=GetSimilarity(data_1, data_2 );
if (minSim > sim) minSim = sim;
}
return minSim;
}
public float GetSimilarity(HierarchicalWordData word1, HierarchicalWordData word2)
{
return(GetSimilarity(word1, word2, 2));
}
/// <summary>
/// Return the similarity of two given words with a taxonomy.
/// </summary>
/// <param name="word1"></param>
/// <param name="word2"></param>
/// <param name="strategy"></param>
/// <returns></returns>
float GetSimilarity(HierarchicalWordData word1, HierarchicalWordData word2, int strategy)
{
if (word1.WordInfo.Pos != word2.WordInfo.Pos || word1.WordInfo.Pos == PartsOfSpeech.Unknown) return 0.0F;
if (word1.WordInfo.Word == word2.WordInfo.Word) return 1.0F;
int pathLength, lcaDepth, depth_1, depth_2;
FindLeastCommonAncestor(new HierarchicalWordData[2] { word1, word2 }, out pathLength, out lcaDepth, out depth_1, out depth_2);
if (pathLength == int.MaxValue) return 0.0F;
float sim=0.0F;
if (strategy == 1)//Path Length
{
if (pathLength == 0) return 1.0F;
else
sim=1.0F / (float)pathLength;
}
else
if (strategy == 2) //Wu & Palmer
{
if (pathLength == 0) return 1.0F;
else
sim=(float)(lcaDepth) / (float)(depth_1 + depth_2);
}
return (float)Math.Round(sim, 2);
}
public float GetSimilarity(HierarchicalWordData word1, HierarchicalWordData word2)
{
return GetSimilarity(word1, word2, 2);
}
public float GetSimilarity(string word1, string word2)
{
Wnlib.PartsOfSpeech[] POSEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
float minSim = float.MaxValue;
for (int partOfSpeech = 1; partOfSpeech < POSEnum.Length; partOfSpeech++)
{
HierarchicalWordData data_1 = new HierarchicalWordData(new MyWordInfo(word1, POSEnum[partOfSpeech]));
HierarchicalWordData data_2 = new HierarchicalWordData(new MyWordInfo(word2, POSEnum[partOfSpeech]));
float sim=GetSimilarity(data_1, data_2 );
if (minSim > sim) minSim = sim;
}
return minSim;
}
/// <summary>
/// Return the least common ancestor/subsummer of two words
/// No unique "join root node" is at present in use.
/// </summary>
/// <param name="words"></param>
/// <param name="distance"></param>
/// <param name="lcaDepth"></param>
/// <param name="depth1"></param>
/// <param name="depth2"></param>
/// <returns></returns>
public long FindLeastCommonAncestor(HierarchicalWordData[] words, out int distance, out int lcaDepth, out int depth1, out int depth2)
{
long LCA = -1;
lcaDepth = -1;
depth1 = -1;
depth2 = -1;
distance = int.MaxValue;
int i=-1;
while (++i < 1 && LCA == -1)
{
IDictionaryEnumerator trackEnum = words[1 - i].Distance.GetEnumerator();
if (trackEnum == null) return -1;
while (trackEnum.MoveNext())
{
int commonAcestor = (int)trackEnum.Key;
if (words[i].Distance.ContainsKey(commonAcestor))
{
int dis_1 = words[i].GetDistance (commonAcestor);
int dis_2 = words[1 - i].GetDistance(commonAcestor);
int len = dis_1 + dis_2 - 1;
if (distance > len)
{
int lcaDepth_1 = words[i].GetDepth(commonAcestor);
int lcaDepth_2 = words[1 - i].GetDepth(commonAcestor);
lcaDepth = lcaDepth_1 + lcaDepth_2;
depth1 = dis_1 + lcaDepth_1 - 1;
depth2 = dis_2 + lcaDepth_2 - 1;
distance = len;
LCA = commonAcestor;
}
}
}
}
return LCA;
}
private string Replace(string[] words, int index)
{
string word = words[index];
WordNetEngine.POS wnepos = StaticHelper.GetWordNetEnginePOS(word);
if (wnepos == WordNetEngine.POS.None) return word;
PartsOfSpeech wnlibpos = StaticHelper.GetWnlibPOSFromWordNetEnginePOS(wnepos);
LAIR.ResourceAPIs.WordNet.SynSet[] synsets = wne.GetSynSets(word, wnepos).ToArray();
double currentSimilarity = THRESHOLD;
List<string> possibleWords = new List<string>();
string sentence = StaticHelper.composer.ComposeSentence(words);
string[] tempWords = words;
foreach (LAIR.ResourceAPIs.WordNet.SynSet synset in synsets)
{
foreach(string possibleWord in synset.Words)
{
if (word.Equals(possibleWord, StringComparison.CurrentCultureIgnoreCase)) continue;
tempWords[index] = StaticHelper.WithoutUnderScore(possibleWord);
string newSentence = StaticHelper.composer.ComposeSentence(tempWords);
SentenceSimilarity ss = new SentenceSimilarity();
double similarity = ss.GetScore(sentence, newSentence);
if(similarity >= currentSimilarity)
{
possibleWords.Add(StaticHelper.WithoutUnderScore(possibleWord));
}
}
}
words[index] = word;
if (possibleWords.Count > 1)
{
WordSimilarity ws = new WordSimilarity();
HierarchicalWordData defaultWord = new HierarchicalWordData(new MyWordInfo(word, StaticHelper.GetWnlibPOS(word)));
currentSimilarity = 0;
string replacement = word;
foreach(string possibleWord in possibleWords)
{
HierarchicalWordData newWord = new HierarchicalWordData(new MyWordInfo(possibleWord, wnlibpos));
double similarity = ws.GetSimilarity(defaultWord, newWord);
if(similarity > currentSimilarity)
{
replacement = possibleWord;
currentSimilarity = similarity;
}
}
return replacement;
}
else if (possibleWords.Count == 1) return possibleWords.ToArray()[0];
else
{
return word;
}
}
//MyWordInfo[] _myWordsInfo_i, _myWordsInfo_j;
//private void MyInitOld()
//{
// _myWordsInfo1 = Disambiguate(_source);
// _myWordsInfo2 = Disambiguate(_target);
// m = _myWordsInfo1.Length; n = _myWordsInfo2.Length;
// _similarity =new float[m, n] ;
// for (int i=0; i < m; i++)
// {
// _myWordsInfo1[i].Sense = _myWordsInfo1[i].Sense < 0 ? 0 : _myWordsInfo1[i].Sense;
// string word1 = _source[i];
// for (int j=0; j < n; j++)
// {
// _myWordsInfo2[i].Sense = _myWordsInfo2[i].Sense < 0 ? 0 : _myWordsInfo2[i].Sense;
// string word2=_target[j];
// WordDistance distance = new WordDistance();
// float weight = distance.GetSimilarity(_myWordsInfo1[i], _myWordsInfo2[j]);
// _similarity[i, j]=weight;
// }
// }
//}
float[][] GetSimilarityMatrix(string[] string1, string[] string2)
{
m = string1.Length; n = string2.Length;
float[][] simMatrix = new float[m][];
Wnlib.PartsOfSpeech[] POSEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
HierarchicalWordData[][] wordData_1 = new HierarchicalWordData[m][];
HierarchicalWordData[][] wordData_2 = new HierarchicalWordData[n][];
for (int i = 0; i < m; i++)
simMatrix[i] = new float[n];
for (int i = 0; i < m; i++)
wordData_1[i] = new HierarchicalWordData[POSEnum.Length];
for (int j = 0; j < n; j++)
wordData_2[j] = new HierarchicalWordData[POSEnum.Length];
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
float synDist = AcronymChecker.GetEditDistanceSimilarity(string1[i], string2[j]);
for (int partOfSpeech = 1; partOfSpeech < POSEnum.Length; partOfSpeech++)
{
if (wordData_1[i][partOfSpeech] == null)
{
MyWordInfo myWordsInfo_i = new MyWordInfo(string1[i], POSEnum[partOfSpeech]);
wordData_1[i][partOfSpeech] = new HierarchicalWordData(myWordsInfo_i);
}
if (wordData_2[j][partOfSpeech] == null)
{
MyWordInfo myWordsInfo_j = new MyWordInfo(string2[j], POSEnum[partOfSpeech]);
wordData_2[j][partOfSpeech] = new HierarchicalWordData(myWordsInfo_j);
}
WordSimilarity wordDistance = new WordSimilarity();
float semDist = wordDistance.GetSimilarity(wordData_1[i][partOfSpeech], wordData_2[j][partOfSpeech]);
float weight = Math.Max(synDist, semDist);
if (simMatrix[i][j] < weight)
simMatrix[i][j] = weight;
}
}
}
return simMatrix;
}
