public MyWordInfo[] FindSynonyms(ref MyWordInfo pos, bool includeMorphs)
{
pos.Word = pos.Word.ToLower();
Wnlib.Index index = Wnlib.Index.lookup( pos.Word, PartOfSpeech.of( pos.Pos ) );
if( index == null )
{
if( !includeMorphs )
return null;
Wnlib.MorphStr morphs = new Wnlib.MorphStr( pos.Word, Wnlib.PartOfSpeech.of( pos.Pos ) );
string morph = "";
while( ( morph = morphs.next() ) != null )
{
index = Wnlib.Index.lookup( morph, Wnlib.PartOfSpeech.of( pos.Pos ) );
pos.Word=morph;
if( index != null )
break;
}
}
if( index == null )
return null;
return LookupCandidates( index, pos );
}
private MyWordInfo[] Disambiguate(string[] words)
{
if (words.Length == 0) return null;
MyWordInfo[] wordInfos=new MyWordInfo [words.Length];
for (int i = 0; i < words.Length; i++)
{
WnLexicon.WordInfo wordInfo = WnLexicon.Lexicon.FindWordInfo(words[i], true);
if (wordInfo.partOfSpeech != Wnlib.PartsOfSpeech.Unknown)
{
if (wordInfo.text != string.Empty)
words[i] = wordInfo.text;
Wnlib.PartsOfSpeech[] posEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
for (int j = 0; j < posEnum.Length; j++)
{
if (wordInfo.senseCounts[j] > 0) // get the first part of speech
{
wordInfos[i] = new MyWordInfo(words[i], posEnum[j]);
break;
}
}
}
}
WordSenseDisambiguator wsd = new WordSenseDisambiguator();
wordInfos=wsd.Disambiguate(wordInfos);
return wordInfos;
}
//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);
}
private int[][][][] _scores;//[i][alter_i][j][alter_j]
public SimilarGenerator(MyWordInfo[] pos, string originalSentence)
{
_myPos=pos;
_originalSentence=originalSentence;
MyInit();
Generate();
list=k_best_sentence;
//
// TODO: Add constructor logic here
//
}
private MyWordInfo[] Disambiguate(string[] words)
{
if (words.Length == 0)
{
return(null);
}
MyWordInfo[] wordInfos = new MyWordInfo [words.Length];
for (int i = 0; i < words.Length; i++)
{
MyWnLexicon.WSDWordInfo wordInfo = MyWnLexicon.Lexicon.FindWordInfo(words[i], true);
if (wordInfo.partOfSpeech != Wnlib.PartsOfSpeech.Unknown)
{
if (wordInfo.text != string.Empty)
{
words[i] = wordInfo.text;
}
Wnlib.PartsOfSpeech[] posEnum = (Wnlib.PartsOfSpeech[])Enum.GetValues(typeof(Wnlib.PartsOfSpeech));
for (int j = 0; j < posEnum.Length; j++)
{
if (wordInfo.senseCounts[j] > 0) // get the first part of speech
{
wordInfos[i] = new MyWordInfo(words[i], posEnum[j]);
break;
}
}
}
}
WordSenseDisambiguator wsd = new WordSenseDisambiguator();
wordInfos = wsd.Disambiguate(wordInfos);
return(wordInfos);
}
private void Add_Sentence(int score)
{
++_numItems ;
MyWordInfo[] pos=new MyWordInfo[_myPos.Length] ;
string newsen="";
string[] words=new string[_numWord] ;
for (int i=0; i <_numWord ; i++)
{
string word=string.Empty ;
if (_selected[i] == -1 )
{
word=_myPos[i].Word;
}
else
{
word=_alterWord[i][_selected[i]].Word;
}
words[i]=word;
pos[i]=new MyWordInfo(word, _myPos[i].Pos) ;
}
newsen=string.Format(_originalSentence, words) ;
if (score > bestScore)
{
bestSentence=newsen + " " + score;
bestScore=score;
}
if (k_best_sentence.Count < 500)
{
newsen=newsen + " " + score;
if (!k_best_sentence.Contains(newsen) )
{
k_best_sentence.Add(newsen);
k_best_score.Add(score);
}
}
else
{
int min=10000000;
int rem=-1;
for (int j=0; j < k_best_sentence.Count ; j++)
{
if ((int)k_best_score[j] < min)
{
min=(int)k_best_score[j];
rem=j;
}
}
newsen=newsen + " " + score;
if (!k_best_sentence.Contains(newsen) && rem != -1)
{
k_best_sentence.RemoveAt(rem) ;
k_best_score.RemoveAt(rem) ;
k_best_sentence.Add(newsen);
k_best_score.Add(score);
}
}
}
private int InitialSentence(out MyWordInfo[] current)
{
current=new MyWordInfo[_myPos.Length] ;
int overall=0;
for (int i=0; i < current.Length; i++)
if (_alterWord[i] != null && _alterWord[i].Length > 0)
{
for (int j = 0; j < _alterWord[i].Length; j++)
{
_selected[i]=j;
_contextWords[i]=_alterWord[i][j];
if (Read_WordSenseInfo (i))
{
int score=ComputeScore(i);
overall +=score;
break;
}
}
}
return overall;
}
private int GetNeighbour(MyWordInfo[] current, out MyWordInfo[] trial)
{
trial=(MyWordInfo[])current.Clone() ;
int wordIndex=random.Next(current.Length);
if (_alterWord[wordIndex] != null )
{
int candIndex=random.Next(_alterWord[wordIndex].Length) ;
_selected[wordIndex]=candIndex;
_contextWords[wordIndex]=_alterWord[wordIndex][candIndex];
if (!Read_WordSenseInfo (wordIndex)) return -1;
trial[wordIndex]=_alterWord[wordIndex][candIndex];
int overall=0;
for (int i=0; i < trial.Length; i++)
{
overall += ComputeScore (i);
}
return overall;
}
return -1;
}
private MyWordInfo[] LookupCandidates(Wnlib.Index index, MyWordInfo pos )
{
if (pos.Sense < 0) pos.Sense=1;
SynSet synset=new Wnlib.SynSet( index.offs[pos.Sense - 1 ], index.pos , index.wd, null , pos.Sense - 1);
ArrayList lexemes=new ArrayList() ;
ArrayList synIndex=new ArrayList() ;
foreach (Lexeme obj in synset.words)
{
lexemes.Add(obj) ;
synIndex.Add(index.offs[pos.Sense - 1 ]);
}
if (index.offs.Length > 1)
{
if (lexemes.Count <= 1)
{
for(int i=0; i < index.offs.Length; i++ )
{
synset=new Wnlib.SynSet( index.offs[i], index.pos, index.wd, null, i );
foreach (Lexeme obj in synset.words)
{
synIndex.Add(index.offs[i]);
lexemes.Add(obj) ;
}
}
}
else
{
synset=new Wnlib.SynSet( index.offs[0], index.pos, index.wd, null, 0 );
int count=0; //get top most frequency word senses
foreach (Lexeme obj in synset.words)
{
lexemes.Add(obj) ;
synIndex.Add(index.offs[0]);
++count;
if (count > 4) break;
}
}
}
ArrayList sortedSet=new ArrayList() ;
Hashtable trace=new Hashtable() ;
int hasSem=0;
for (int i = 0; i < lexemes.Count; i++)
{
Lexeme word=(Lexeme)lexemes[i];
word.word=word.word.ToLower() ;
int senIndex=(int)synIndex[i];
if (senIndex != -1 && word.wnsns > 0)
{
word.semcor=new Wnlib.SemCor(word, senIndex);
lexemes[i]=word;
++hasSem;
}
if (!trace.ContainsKey(word.word) )
{
if ((word.semcor != null && word.semcor.semcor > 0 ) || (hasSem < 4))
{
trace[word.word]=1;
sortedSet.Add(word) ;
}
}
//catch
{}
}
Lexeme[] words=(Lexeme[])sortedSet.ToArray( typeof(Lexeme) );
ArrayList candidates=new ArrayList();
for( int i=0; i < words.Length; i++ )
{
string word=words[i].word.Replace("_", " " );
if( word[0] <= 'Z' ) continue;
MyWordInfo newpos=new MyWordInfo(word, pos.Pos) ;
newpos.Sense=words[i].wnsns;
if (words[i].semcor != null)
newpos.Frequency=words[i].semcor.semcor;
else
newpos.Frequency=0;
candidates.Add( newpos);
}
if (!trace.ContainsKey (index.wd))
candidates.Add(pos) ;
if (candidates.Count > 1)
{
CompareLexeme comparer=new CompareLexeme();
candidates.Sort(comparer);
}
return (MyWordInfo[])candidates.ToArray( typeof(MyWordInfo) );
}
public MyWordInfo[] Disambiguate(MyWordInfo[] words)
{
_contextWords=words;
MyInit();
Scoring_Overlaps();
for (int i=0; i < _contextWords.Length ; i++)
_contextWords[i].Sense=_bestSenses[i];
return _contextWords;
}
public HierarchicalWordData(MyWordInfo wordInfo)
{
this.WordInfo = wordInfo;
Build_WordData();
}
public HierarchicalWordData(MyWordInfo wordInfo)
{
this.WordInfo = wordInfo;
Build_WordData();
}
//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 GetWordNetSimScore(MyWordInfo[] words1, MyWordInfo[] words2, string key)
{
float[][] simMatrix = SentenceSim.GetSimilarityMatrix(words1, words2);
return GetSimScore(simMatrix);
}