// get the wordVec
// lowercase the word
public virtual DoubleVec GetWordVec(string word)
{
string inWord = GetKeyWord(word);
DoubleVec @out = wordVecMap_.GetValueOrNull(inWord);
return(@out);
}
private static void TestOnSet(Word2Vec w2vIm, Word2Vec w2vOm)
{
string inText = "He was diagnosed early on set dementia 3 years ago.";
TextObj textObj = new TextObj(inText);
List <TokenObj> inTextList = textObj.GetTokenList();
// remove space token from the list
List <TokenObj> nonSpaceTokenList = TextObj.GetNonSpaceTokenObjList(inTextList);
Console.WriteLine("==========================================");
Console.WriteLine("-- inTextList: [" + inText + "]");
int tarPos = 4;
int tarSize = 2; // "on set" has 2 tokens
int radius = 2;
bool word2VecSkipWord = true;
bool debugFlag = false;
// 1 context with window radius
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, w2vIm, radius, word2VecSkipWord, debugFlag);
string str1 = "onset";
ContextScore s1 = new ContextScore(str1, contextVec, w2vOm);
string str2 = "on set";
ContextScore s2 = new ContextScore(str2, contextVec, w2vOm);
Console.WriteLine("- [" + str1 + "]: " + s1.ToString());
Console.WriteLine("- [" + str2 + "]: " + s2.ToString());
}
private static void Test(string inTerm, DoubleVec contextVec, Word2Vec w2vIm, Word2Vec w2vOm)
{
double cwobScore = GetScore(inTerm, contextVec, w2vOm);
double cwobScore2 = GetScore2(inTerm, contextVec, w2vOm);
double cosScore = GetSimilarityScore(inTerm, contextVec, w2vIm);
Console.WriteLine(inTerm + "|" + string.Format("{0,1:F8}", cwobScore) + "|" + string.Format("{0,1:F8}", cwobScore2) + "|" + string.Format("{0,1:F8}", cosScore));
}
// try two different method, inner dot or cos
private static double GetCwobScore(DoubleVec vec1, DoubleVec vec2)
{
//double score = DoubleVecUtil.GetInnerDot(vec1, vec2);
// normailzed score between -1.0 to 1.0
double score = DoubleVecUtil.GetCosineSimilarity(vec1, vec2);
return(score);
}
// private method
private static double GetSimilarityScore(DoubleVec vec1, DoubleVec vec2)
{
double score = DoubleVecUtil.GetCosineSimilarity(vec1, vec2);
// set the min. to 0.0
score = (score <= 0.0 ? 0.0d : score);
return(score);
}
// public constructor
/// <summary>
/// Public constructor for ContextScore
/// </summary>
/// <param name="inTerm"> target token or candidate (can be multiword) </param>
/// <param name="contextVec"> wordVec of context from IM </param>
/// <param name="word2Vec"> word2Vec matrix of OM </param>
public ContextScore(string inTerm, DoubleVec contextVec, Word2Vec word2Vec)
{
term_ = inTerm;
// Use Cosine Similarity between IM and OM
score_ = Word2VecScore.GetScore(inTerm, contextVec, word2Vec);
// TBD: use 2-3 gram
//score_ = NgramScore.GetScore(word, ngram);
}
// use cosineSimilarity instead of inner dot
private static double GetInnerDotScore(DoubleVec vec1, DoubleVec vec2)
{
double score = DoubleVecUtil.GetInnerDot(vec1, vec2);
// set the min. to 0.0
// it seems -score is useful for split ....
//score = (score <= 0.0?0.0d:score);
return(score);
}
// from ensemble paper, use the word vector (Input Matrix) for w2v
// word2VecIm: word2Vec input matrix - syn0
// Similarity score use word2Vec Im
public static double GetSimilarityScore(string inTerm, DoubleVec contextVec, Word2Vec word2VecIm)
{
// 1. Get Avg. score for inTerm
DoubleVec termVec = GetWordVecForTerm(inTerm, word2VecIm);
// 2. Get Cosine similarity between contextVec and tarVec
double score = GetSimilarityScore(termVec, contextVec);
return(score);
}
// Use Avg. word2Vec Om for each word in the inTerm
private static DoubleVec GetWordVecForTerm(string inTerm, Word2Vec w2vOm)
{
List <string> inWordList = TermUtil.ToWordList(inTerm);
// avg. the wordVec if inTerm is a multiword
DoubleVec outWordVec = GetAvgWordVecForList(inWordList, w2vOm);
// TBD: take care of possesive
return(outWordVec);
}
// context from all inTextList, no specify on window radius
public static DoubleVec GetContextVec(int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec w2vIm, bool word2VecSkipWord, bool debugFlag)
{
// 1. get the context
List <string> contextList = GetContext(tarPos, tarSize, nonSpaceTokenList, w2vIm, word2VecSkipWord, debugFlag);
// 2. get the wordVec for the context
DoubleVec contextVec = Word2VecScore.GetAvgWordVecForList(contextList, w2vIm);
return(contextVec);
}
// public method
// inTerm: candidate (can be multiword)
// contextVec: wordVec of context
// word2VecOm: word2Vec output matrix - syn1neg
// Use CWOB model to predict the target word = H X OM
public static double GetScore(string inTerm, DoubleVec contextVec, Word2Vec w2vOm)
{
// 1. Get Avg. Vec for term (candidate from prediction)
DoubleVec termVec = GetWordVecForTerm(inTerm, w2vOm);
// 2. got the inner dot between hidden layer (context) and OM
// to predict the output matrix in CBOW
double score = GetCwobScore(termVec, contextVec);
return(score);
}
// private constructor
public CSpellScore(string wordStr, string candStr, WordWcMap wordWcMap, DoubleVec contextVec, Word2Vec word2Vec, double wf1, double wf2, double wf3)
{
wordStr_ = wordStr;
candStr_ = candStr;
// calculate score
oScore_ = new OrthographicScore(wordStr_, candStr_, wf1, wf2, wf3);
fScore_ = new FrequencyScore(candStr_, wordWcMap);
nScore_ = new NoisyChannelScore(wordStr_, candStr_, wordWcMap, wf1, wf2, wf3);
cScore_ = new ContextScore(candStr_, contextVec, word2Vec);
}
// return the best ranked str from candidates using word2Vec score
// inTokenList, includes space token, is not coreTerm.Lc
// return null if no candidate is found to correct
public static MergeObj GetTopRankMergeObj(HashSet <MergeObj> candidates, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, double rwMergeFactor, bool debugFlag)
{
// init the topRankMergeObj
MergeObj topRankMergeObj = null;
if (candidates.Count > 0)
{
// 1. find sorted score list for each candidates ...
List <ContextScore> candScoreList = GetCandidateScoreList(candidates, nonSpaceTokenList, word2VecIm, word2VecOm, word2VecSkipWord, contextRadius, debugFlag);
// 2. find the top ranked str
// the 0 element has the highest score because it is sorted
// only 1 candidate, use it for nonWord
ContextScore topContextScore = null;
if (candScoreList.Count > 0)
{
topContextScore = candScoreList[0];
}
// 3. find the mergeObj from the topRankStr (if exist)
if (topContextScore != null)
{
// 3.1. convert mergeObj set to string set
// key: coreMergeWord, MergeObj
Dictionary <string, MergeObj> candStrMergeObjMap = new Dictionary <string, MergeObj>();
foreach (MergeObj mergeObj in candidates)
{
string mergeWord = mergeObj.GetCoreMergeWord();
candStrMergeObjMap[mergeWord] = mergeObj;
}
HashSet <string> andStrSet = new HashSet <string>(candStrMergeObjMap.Keys);
// 3.2 convert back from top rank str to MergeObj
// topRankStr should never be null because candidates is > 0
string topRankStr = topContextScore.GetTerm();
topRankMergeObj = candStrMergeObjMap.GetValueOrNull(topRankStr);
// 4. compare the top rank merge to the original string b4 merge
// 1. get the word2Vec score for the orgMergeTerm b4 merge
// 1.1 wordVec for context
int tarPos = topRankMergeObj.GetStartPos();
// tarSize is the total token No of the orgMergeWords
int tarSize = topRankMergeObj.GetEndPos() - topRankMergeObj.GetStartPos() + 1;
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 1.2 wordVec for the original words before merge
string orgMergeWord = topRankMergeObj.GetOrgMergeWord();
ContextScore orgContextScore = new ContextScore(orgMergeWord, contextVec, word2VecOm);
// validate top merge candidate, set to null if false
if (IsTopCandValid(orgContextScore, topContextScore, rwMergeFactor, debugFlag) == false)
{
// set to null if score is not good enough for corection
topRankMergeObj = null;
}
}
}
return(topRankMergeObj);
}
// private method
// Test merge and Split
private static void Test(string inText, int tarPos, int tarSize, int radius, string mergedWord, string splitWords, Word2Vec w2vIm, Word2Vec w2vOm)
{
// 0. process the inText
TextObj textObj = new TextObj(inText);
List <TokenObj> inTextList = textObj.GetTokenList();
// remove space token from the list
List <TokenObj> nonSpaceTokenList = TextObj.GetNonSpaceTokenObjList(inTextList);
Console.WriteLine("==========================================");
Console.WriteLine("-- inTextList: [" + inText + "]");
bool word2VecSkipWord = true;
bool debugFlag = false;
// 1.a context with window radius
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, w2vIm, radius, word2VecSkipWord, debugFlag);
// 1.b context with all inText
DoubleVec contextVecA = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, w2vIm, word2VecSkipWord, debugFlag);
// 1.c get score1
ContextScore score1 = new ContextScore(mergedWord, contextVec, w2vOm);
ContextScore score1a = new ContextScore(mergedWord, contextVecA, w2vOm);
Console.WriteLine(score1.ToString() + "|" + string.Format("{0,1:F8}", score1a.GetScore()));
// 2. split words
ContextScore score2 = new ContextScore(splitWords, contextVec, w2vOm);
ContextScore score2a = new ContextScore(splitWords, contextVecA, w2vOm);
Console.WriteLine(score2.ToString() + "|" + string.Format("{0,1:F8}", score2a.GetScore()));
// 3. 3. 3. Use avg. score on single words
// This method use different context for each single word
List <string> splitWordList = TermUtil.ToWordList(splitWords);
int index = 0;
double scoreSAvg = 0.0d; // radius
double scoreSAAvg = 0.0d; // all inText
//debugFlag = false;
foreach (string splitWord in splitWordList)
{
// window radius
DoubleVec contextVecS = Word2VecContext.GetContextVec(tarPos + index, 1, nonSpaceTokenList, w2vIm, radius, word2VecSkipWord, debugFlag);
ContextScore scoreS = new ContextScore(splitWord, contextVecS, w2vOm);
//System.out.println("-- " + scoreS.ToString());
scoreSAvg += scoreS.GetScore();
// all text
DoubleVec contextVecSA = Word2VecContext.GetContextVec(tarPos + index, 1, nonSpaceTokenList, w2vIm, word2VecSkipWord, debugFlag);
ContextScore scoreSA = new ContextScore(splitWord, contextVecSA, w2vOm);
//System.out.println("-- " + scoreSA.ToString());
scoreSAAvg += scoreSA.GetScore();
index++;
}
scoreSAvg = scoreSAvg / index; // window
scoreSAAvg = scoreSAAvg / index; // all text
Console.WriteLine("Avg. Single Word|" + string.Format("{0,1:F8}", scoreSAvg) + "|" + string.Format("{0,1:F8}", scoreSAAvg));
}
private static void TestWin(string inTerm, DoubleVec contextVec, Word2Vec w2vIm, Word2Vec w2vOm)
{
double cwobScore = GetScore(inTerm, contextVec, w2vOm);
double cwobScore2 = GetScore2(inTerm, contextVec, w2vOm);
double cosScore = GetSimilarityScore(inTerm, contextVec, w2vIm);
Console.Write(string.Format("{0,1:F4}", cwobScore) + "-" + string.Format("{0,1:F4}", cwobScore2) + "|");
/*
* System.out.println(inTerm + "|" + String.format("%1.8f", cwobScore)
+ "|" + String.format("%1.8f", cosScore));
*/
}
// return candidate set with cSpell score
// wordStr is the srcTxt used to calculate the score between it and cand
public static HashSet <CSpellScore> GetCandidateScoreSet(string wordStr, HashSet <string> candidates, WordWcMap wordWcMap, int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, double wf1, double wf2, double wf3, bool debugFlag)
{
HashSet <CSpellScore> candScoreSet = new HashSet <CSpellScore>();
foreach (string cand in candidates)
{
// find context for each candidates
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
CSpellScore cs = new CSpellScore(wordStr, cand, wordWcMap, contextVec, word2VecOm, wf1, wf2, wf3);
candScoreSet.Add(cs);
}
return(candScoreSet);
}
private static bool CheckRealWord1To1Rules(ContextScore topContextScore, string inStr, int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, double rw1To1Factor, bool debugFlag)
{
// return false if no topCand found
if ((topContextScore == null) || (topContextScore.GetTerm().Equals(inStr)))
{
return(false);
}
// 1. get the word2Vec score for the org inStr b4 one-to-one
// 1.1 wordVec for context
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 1.2 wordVec for the original words before one-to-one
ContextScore orgCs = new ContextScore(inStr, contextVec, word2VecOm);
DebugPrint.Println("--- Real-Word One-To-One Context Score Detail: ---", debugFlag);
DebugPrint.Println("- Score - orgTerm: " + orgCs.ToString(), debugFlag);
DebugPrint.Println("- Score - top 1-to-1: " + topContextScore.ToString(), debugFlag);
DebugPrint.Println("- rw1To1Factor: " + rw1To1Factor, debugFlag);
// Score rules for one-to-one
double orgScore = orgCs.GetScore();
double topScore = topContextScore.GetScore();
bool flag = false;
// 2.1 no one-to-one correction if orgScore is 0.0d, no word2Vec information
if (orgScore < 0.0d)
{
// 2.2a one-to-one if the org score is negative and top score is positive
if (topScore > 0.0d)
{
// another rule for word2Vec on real-word
if (((topScore - orgScore) > 0.085) && (orgScore > -0.085)) // help from 0.6812 to 0.6877
{
flag = true;
}
}
// 2.2b one-to-one if the org score is negative and top score is better
else if ((topScore < 0.0d) && (topScore > orgScore * rw1To1Factor))
{
flag = true;
}
}
else if (orgScore > 0.0d)
{
// 2.3a merge if the org score is positive and better 0.01*topScore
if (topScore * rw1To1Factor > orgScore)
{
flag = true;
}
}
return(flag);
}
private void ReadWordVectors(string inFile, bool verboseFlag)
{
// init
wordVecMap_ = new Dictionary <string, DoubleVec>();
int lineNo = 0;
string line = null;
try {
StreamReader @in = new StreamReader(new FileStream(inFile, FileMode.Open, FileAccess.Read), Encoding.UTF8);
// read in line by line from a file
while (!string.ReferenceEquals((line = @in.ReadLine()), null))
{
var buf = line.Split(" ");
// first line is the stats wordNo|dimension
if (lineNo == 0)
{
wordNo_ = int.Parse(buf[0]);
dimension_ = int.Parse(buf[1]);
//dimension_ = line0.split(delimiter).length -1;
}
else // word|vector
{
string word = buf[0];
double[] array = new double[dimension_];
for (int i = 0; i < dimension_; i++)
{
array[i] = double.Parse(buf[i + 1]);
}
// update wordVec
wordVecMap_[word] = new DoubleVec(array);
}
lineNo++;
}
// close
@in.Close();
} catch (IOException e) {
Console.Error.WriteLine("** [email protected]( ), problem of reading file (" + inFile);
Console.Error.WriteLine("Line: " + lineNo + " - " + line);
Console.Error.WriteLine("Exception: " + e.ToString());
}
// print out
if (verboseFlag == true)
{
Console.WriteLine("====== Word2Vec.ReadWordVectors( ) ======");
Console.WriteLine("- inFile: " + inFile);
Console.WriteLine("- Word no: " + wordNo_);
Console.WriteLine("- dimension: " + dimension_);
}
}
// return candidate set with context score
// word2Vec is the word|wordVec map to get the wordVec
// Not sorted, because it is a set
// tarPos: starting position of target token
// tarSize: token size of target token (single word = 1)
// contextRadius: windown radius
public static HashSet <ContextScore> GetCandidateScoreSet(HashSet <string> candidates, int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, bool debugFlag)
{
// 1. get the context and contextVec, using input matrix
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 2. get context score for all candidates
HashSet <ContextScore> candScoreSet = new HashSet <ContextScore>();
foreach (string cand in candidates)
{
// get ContextSocre for each candidates, use output matrix
ContextScore cs = new ContextScore(cand, contextVec, word2VecOm);
candScoreSet.Add(cs);
}
return(candScoreSet);
}
// return candidate set with context score
// word2Vec is the word|wordVec map to get the wordVec
// Not sorted, because it is a set
// tarPos: starting position of target token
// tarSize: token size of target token (single word = 1)
public static HashSet <ContextScore> GetCandidateScoreSet(HashSet <MergeObj> candidates, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, bool debugFlag)
{
HashSet <ContextScore> candScoreSet = new HashSet <ContextScore>();
// get context score for all candidates
// go through all merge candidates, all have differetn context
foreach (MergeObj mergeObj in candidates)
{
// 1. get the context and contextVec, using input matrix
int tarPos = mergeObj.GetStartPos();
int tarSize = mergeObj.GetEndPos() - mergeObj.GetStartPos() + 1;
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 2. get ContextSocre for each merge, use output matrix
string mergeWord = mergeObj.GetCoreMergeWord();
ContextScore cs = new ContextScore(mergeWord, contextVec, word2VecOm);
candScoreSet.Add(cs);
}
return(candScoreSet);
}
// this method is to be deleted because it has same result as GetScore()
public static double GetScore2(string inTerm, DoubleVec contextVec, Word2Vec w2vOm)
{
List <string> inWordList = TermUtil.ToWordList(inTerm);
double score = 0.0d;
int count = 0;
foreach (string word in inWordList)
{
DoubleVec wordVec = w2vOm.GetWordVec(word);
if (wordVec != null)
{
score += GetCwobScore(wordVec, contextVec);
}
count++;
}
// add score first, then calculate the avg.
score = score / count;
return(score);
}
// return the best ranked str from candidates using context score
// this method is replaced by GetTopRankStr, which sorted by comparator
public static string GetTopRankStrByScore(string inStr, HashSet <string> candidates, int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, bool debugFlag)
{
// 1. get the context and contextVec
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
string topRankStr = inStr;
double maxScore = 0.0d;
foreach (string cand in candidates)
{
ContextScore cs = new ContextScore(cand, contextVec, word2VecOm);
double score = cs.GetScore();
// update only if the score is > 0.0d
if (score > maxScore)
{
topRankStr = cand;
maxScore = score;
}
}
return(topRankStr);
}
// return the best ranked str from candidates using word2Vec score
// inTokenList, includes space token, is not coreTerm.Lc
// return the orignal inStr if no candidate has score > 0.0d
public static string GetTopRankStr(string inStr, HashSet <string> candidates, int tarPos, int tarSize, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, int shortSplitWordLength, int maxShortSplitWordNo, double rwSplitFactor, int maxCandNo, bool debugFlag)
{
// init
string topRankStr = inStr;
// Find the correction str
if (candidates.Count > 0)
{
// 1. sorted score list for each candidates ...
// This ranking can be improved if n-gram model (frequecny) is used
List <ContextScore> candScoreList = RankByContext.GetCandidateScoreList(candidates, tarPos, tarSize, nonSpaceTokenList, word2VecIm, word2VecOm, word2VecSkipWord, contextRadius, debugFlag);
// 1.1 get the top tank candidate
ContextScore topContextScore = candScoreList[0];
// 2. validate the top rank
// 2.1 wordVec for context
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 2.2 wordVec for the original words before split
ContextScore orgContextScore = new ContextScore(inStr, contextVec, word2VecOm);
// 2.3 compare the top rank split to the original string b4 split
if (IsTopCandValid(inStr, orgContextScore, topContextScore, rwSplitFactor, debugFlag) == true)
{
// no correction: if score is not good enough for corection
topRankStr = topContextScore.GetTerm();
}
// debug print
if (debugFlag == true)
{
// print focus token (original)
DebugPrint.PrintCScore(orgContextScore.ToString(), debugFlag);
// print candidates
ContextScoreComparator <ContextScore> csc = new ContextScoreComparator <ContextScore>();
var list = candScoreList.OrderBy(x => x, csc).Take(maxCandNo).Select(obj => obj.ToString()).ToList();
foreach (var item in list)
{
DebugPrint.PrintCScore(item, debugFlag);
}
}
}
return(topRankStr);
}
// Average wordVec for a list of words
public static DoubleVec GetAvgWordVecForList(IList <string> wordList, Word2Vec word2Vec)
{
// init the matrix to all zero
int dimension = word2Vec.GetDimension();
DoubleVec aveWordVec = new DoubleVec(dimension);
int count = 0;
foreach (string word in wordList)
{
DoubleVec wordVec = word2Vec.GetWordVec(word);
if (wordVec != null)
{
aveWordVec.Add(wordVec);
}
count++;
}
// calculate the avg.
if (count != 0)
{
aveWordVec.Divide(count);
}
return(aveWordVec);
}
// return the best ranked str from candidates using context score
// this method is replaced by GetTopRankStr, which sorted by comparator
public static MergeObj GetTopRankMergeObjByScore(HashSet <MergeObj> candidates, List <TokenObj> nonSpaceTokenList, Word2Vec word2VecIm, Word2Vec word2VecOm, bool word2VecSkipWord, int contextRadius, bool debugFlag)
{
MergeObj topRankMergeObj = null;
double maxScore = 0.0d;
foreach (MergeObj mergeObj in candidates)
{
// 1. get the context and contextVec
int tarPos = mergeObj.GetStartPos();
int tarSize = mergeObj.GetEndPos() - mergeObj.GetStartPos() + 1;
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 2. get ContextSocre for each merge, use output matrix
string mergeWord = mergeObj.GetCoreMergeWord();
ContextScore cs = new ContextScore(mergeWord, contextVec, word2VecOm);
double score = cs.GetScore();
// update only if the score is > 0.0d
if (score > maxScore)
{
topRankMergeObj = mergeObj;
maxScore = score;
}
}
return(topRankMergeObj);
}
private static void Tests(Word2Vec w2vIm, Word2Vec w2vOm)
{
string inText = "for the last 10 years was dianosed\n early on set deminita 3 years ago";
List <TokenObj> inTextList = TextObj.TextToTokenList(inText);
// remove space token from the list
List <TokenObj> nonSpaceTokenList = TextObj.GetNonSpaceTokenObjList(inTextList);
List <string> testStrList = new List <string>();
testStrList.Add("diagnosed");
testStrList.Add("diagnose");
testStrList.Add("dianosed");
// init context
int tarPos = 6;
int tarSize = 1;
int radius = 2;
bool word2VecSkipWord = true;
bool debugFlag = false;
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, w2vIm, radius, word2VecSkipWord, debugFlag);
Console.WriteLine("===== Test diagnosed|diagnose|dianosed (window-2) =====");
Console.WriteLine("inText: [" + inText + "]");
Console.WriteLine("============================================");
Console.WriteLine("Candidates|CBOW score|CBOW score 2|Similarity score");
Console.WriteLine("============================================");
foreach (string testStr in testStrList)
{
Test(testStr, contextVec, w2vIm, w2vOm);
}
Console.WriteLine("===== Test diagnosed|diagnose|dianosed (whole text) =====");
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, w2vIm, word2VecSkipWord, debugFlag);
foreach (string testStr in testStrList)
{
Test(testStr, contextVec, w2vIm, w2vOm);
}
string inText1 = "Not all doctors know about this syndrome.";
List <TokenObj> inTextList1 = TextObj.TextToTokenList(inText1);
// remove space token from the list
List <TokenObj> nonSpaceTokenList1 = TextObj.GetNonSpaceTokenObjList(inTextList1);
Console.WriteLine("===== Test know about|know|about (window) =====");
List <string> testStrList1 = new List <string>();
testStrList1.Add("know about");
testStrList1.Add("know");
testStrList1.Add("about");
tarPos = 3;
tarSize = 2;
radius = 2;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, radius, word2VecSkipWord, debugFlag);
Test(testStrList1[0], contextVec, w2vIm, w2vOm);
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, word2VecSkipWord, debugFlag);
Test(testStrList1[0], contextVec, w2vIm, w2vOm);
tarPos = 3;
tarSize = 1;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, radius, word2VecSkipWord, debugFlag);
Test(testStrList1[1], contextVec, w2vIm, w2vOm);
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, word2VecSkipWord, debugFlag);
Test(testStrList1[1], contextVec, w2vIm, w2vOm);
tarPos = 4;
tarSize = 1;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, radius, word2VecSkipWord, debugFlag);
Test(testStrList1[2], contextVec, w2vIm, w2vOm);
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList1, w2vIm, word2VecSkipWord, debugFlag);
Test(testStrList1[2], contextVec, w2vIm, w2vOm);
string inText2 = "for the last 10 years was diagnosed early on set dementia 3 years ago.";
List <TokenObj> inTextList2 = TextObj.TextToTokenList(inText2);
// remove space token from the list
List <TokenObj> nonSpaceTokenList2 = TextObj.GetNonSpaceTokenObjList(inTextList2);
List <string> testStrList2 = new List <string>();
testStrList2.Add("onset");
testStrList2.Add("on set");
Console.WriteLine("===== Test onset|on set (window-3) =====");
Console.WriteLine("inText: [" + inText + "]");
tarPos = 8;
tarSize = 2;
radius = 3;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList2, w2vIm, radius, word2VecSkipWord, debugFlag);
foreach (string testStr in testStrList2)
{
Test(testStr, contextVec, w2vIm, w2vOm);
}
tarPos = 8;
tarSize = 1;
radius = 3;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList2, w2vIm, radius, word2VecSkipWord, debugFlag);
Test("on", contextVec, w2vIm, w2vOm);
tarPos = 9;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList2, w2vIm, radius, word2VecSkipWord, debugFlag);
Test("set", contextVec, w2vIm, w2vOm);
Console.WriteLine("===== Test onset|on set (whole text) =====");
radius = nonSpaceTokenList2.Count;
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList2, w2vIm, word2VecSkipWord, debugFlag);
foreach (string testStr in testStrList2)
{
Test(testStr, contextVec, w2vIm, w2vOm);
}
Console.WriteLine("===== Go through each tokens with diff radius 1-9) =====");
Console.WriteLine("tarPos|tarWord|r=1|r=2|r=3|r=4|r=5|r=6|r=7|r=8|r=9");
//String inText3 = "Broken bones can not sleep at night!";
string inText3 = "not xyxy all doctors know about this syndrome.";
List <TokenObj> inTextList3 = TextObj.TextToTokenList(inText3);
// remove space token from the list
List <TokenObj> nonSpaceTokenList3 = TextObj.GetNonSpaceTokenObjList(inTextList3);
tarPos = 0;
tarSize = 1;
radius = 0;
foreach (TokenObj tokenObj in nonSpaceTokenList3)
{
// skip the space token
string tokenStr = tokenObj.GetTokenStr();
string inStr = Word2VecContext.NormWordForWord2Vec(tokenStr);
Console.Write(tarPos + "|" + tokenStr + "|");
// print out all radius
for (int r = 1; r < 10; r++)
{
contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, inTextList2, w2vIm, r, word2VecSkipWord, debugFlag);
TestWin(inStr, contextVec, w2vIm, w2vOm);
}
Console.WriteLine("");
tarPos++;
}
}
// return the best ranked str from candidates using word2Vec score
// inTokenList, includes space token, is not coreTerm.Lc
// return the orignal inStr if no candidate has score > 0.0d
public static string GetTopRankStr(string inStr, HashSet <string> candidates, CSpellApi cSpellApi, int tarPos, List <TokenObj> nonSpaceTokenList, bool debugFlag)
{
// init
WordWcMap wordWcMap = cSpellApi.GetWordWcMap();
Word2Vec word2VecIm = cSpellApi.GetWord2VecIm();
Word2Vec word2VecOm = cSpellApi.GetWord2VecOm();
int contextRadius = cSpellApi.GetRw1To1ContextRadius();
bool word2VecSkipWord = cSpellApi.GetWord2VecSkipWord();
int maxCandNo = cSpellApi.GetCanMaxCandNo();
double wf1 = cSpellApi.GetOrthoScoreEdDistFac();
double wf2 = cSpellApi.GetOrthoScorePhoneticFac();
double wf3 = cSpellApi.GetOrthoScoreOverlapFac();
int tarSize = 1; // only for one-to-one, no merge here
string topRankStr = inStr;
// use cSpell top candidates
int topNo = 1; // top sort
string inStrLc = inStr.ToLower();
List <CSpellScore> cSpellScoreList = RankByCSpellRealWord1To1.GetCandidateScoreList(inStrLc, candidates, wordWcMap, tarPos, tarSize, nonSpaceTokenList, word2VecIm, word2VecOm, word2VecSkipWord, contextRadius, wf1, wf2, wf3, debugFlag);
// Find the correction str and correct
if (cSpellScoreList.Count > 0)
{
// the rw top rank must be in both NC and orthographic
CSpellScore topScore = cSpellScoreList[0];
double topFScore = topScore.GetFScore().GetScore(); //frequency
double topTScore = topScore.GetOScore().GetTokenScore(); // Token
double topPScore = topScore.GetOScore().GetPhoneticScore(); //Phone
double topOScore = topScore.GetOScore().GetOverlapScore(); //overlap
ContextScore orgContextScore = null;
// check the frequency
// get the max score of frequency, eidt, phonetic, and overlap
// the top rank must have all top score for above
if ((topFScore == CSpellScore.GetMaxFScore(cSpellScoreList)) && (topTScore == CSpellScore.GetMaxEScore(cSpellScoreList)) && (topPScore == CSpellScore.GetMaxPScore(cSpellScoreList)) && (topOScore == CSpellScore.GetMaxOScore(cSpellScoreList)))
{
ContextScore topContextScore = topScore.GetCScore();
// 1.1 wordVec for context
DoubleVec contextVec = Word2VecContext.GetContextVec(tarPos, tarSize, nonSpaceTokenList, word2VecIm, contextRadius, word2VecSkipWord, debugFlag);
// 1.2 wordVec for the original words before one-to-one
orgContextScore = new ContextScore(inStr, contextVec, word2VecOm);
FrequencyScore orgFScore = new FrequencyScore(inStr, wordWcMap);
// pass the orgContextScore
if (IsTopCandValid(inStr, orgContextScore, topScore, orgFScore, cSpellApi, debugFlag) == true)
{
// no correction: if score is not good enough for corection
topRankStr = topScore.GetCandStr();
// debug print for ananlysis
/// <summary>
///*
/// System.out.println("======= cSpellScoreList.size(): "
/// + cSpellScoreList.size() + " ========");
/// System.out.println(inStr
/// + "," + String.format("%1.8f", orgFScore.GetScore())
/// + "," + String.format("%1.8f", orgContextScore.GetScore()));
/// System.out.println(CSpellScore.GetScoreHeader());
/// for(CSpellScore cSpellScore: cSpellScoreList)
/// {
/// System.out.println(cSpellScore.ToString(","));
/// }
/// **
/// </summary>
}
}
// debug print
if (debugFlag == true)
{
// print focus token (original)
if (orgContextScore != null)
{
DebugPrint.PrintScore(orgContextScore.ToString(), debugFlag);
}
else
{
DebugPrint.PrintScore("No score for focus (" + inStr + ")", debugFlag);
}
// print candidate
var list = cSpellScoreList.Take(maxCandNo).Select(obj => obj.ToString()).ToList();
foreach (var item in list)
{
DebugPrint.PrintScore(item, debugFlag);
}
}
}
return(topRankStr);
}
