private static void CreateList()
{
StreamReader sr = new StreamReader("corpus.txt"); /** Change textfile name from here. **/
string line;
while ((line = sr.ReadLine()) != null)
{
CorpusWord cw = new CorpusWord();
List <string> tokens = line.Split(' ').ToList();
if (tokens.Count > 1)
{
while (tokens.Count > 2)
{
tokens[0] = tokens[0] + " " + tokens[1];
tokens[1] = tokens[2];
if (tokens.Count > 3)
{
tokens[2] = tokens[3];
}
tokens.RemoveAt(tokens.Count - 1);
}
cw.Attribute = CorpusWord.ToAttribute(tokens[1]);
}
else
{
cw.Attribute = Attribute.NULL;
}
cw.Word = tokens[0];
corpusList.Add(cw);
}
count = corpusList.Count;
sr.Close();
}
public static void Train()
{
SVMProblem svmproblem = new SVMProblem();
List <Tuple <Sentence, string> > sentences = new List <Tuple <Sentence, string> >();
StreamReader sr = new StreamReader("training_data.txt");
string line;
while ((line = sr.ReadLine()) != null)
{
string[] lines = line.Split(',');
lines[0] = lines[0].Substring(1, lines[0].Length - 2);
string sentence = lines[0];
sentences.Add(Tuple.Create(Sentence.ParseIntoSentence(sentence, 0, false), lines[1]));
}
List <SVMNode[]> modelNodes = new List <SVMNode[]>();
for (int i = 0; i < sentences.Count; i++)
{
List <SVMNode> nodes = new List <SVMNode>();
for (int j = 0; j < Corpus.count; j++)
{
SVMNode sentenceNode = new SVMNode(j, 0);
nodes.Add(sentenceNode);
}
for (int j = 0; j < sentences[i].Item1.SentenceWords.Count; j++)
{
CorpusWord cw = Corpus.CorpusList.Find(c => c.Word.Equals(sentences[i].Item1.SentenceWords[j].Stem.Word) && c.Attribute.Equals(sentences[i].Item1.SentenceWords[j].Stem.Attribute));
SVMNode sentenceNode = new SVMNode(Corpus.CorpusList.IndexOf(cw), 1);
SVMNode sentenceNodeToRemove = new SVMNode(Corpus.CorpusList.IndexOf(cw), 0);
nodes.Remove(sentenceNodeToRemove);
nodes.Add(sentenceNode);
}
SVMNode[] sentenceNodes = nodes.ToArray();
sentenceNodes = sentenceNodes.OrderBy(x => x.Index).ToArray();
svmproblem.Add(sentenceNodes, Corpus.CorpusList.IndexOf(Corpus.CorpusList.Find(c => c.Word.Equals(sentences[i].Item2))));
}
model = SVM.Train(svmproblem, parameter);
sr.Close();
}
private void button1_Click(object sender, EventArgs e)
{
if (field1.Text != "" && field2.Text != "")
{
sentence1 = Sentence.ParseIntoSentence(field1.Text, 1, true);
sentence2 = Sentence.ParseIntoSentence(field2.Text, 2, true);
if (sentence1 != null && sentence2 != null)
{
List <SVMNode> nodes = new List <SVMNode>();
for (int j = 0; j < Corpus.count; j++)
{
SVMNode sentenceNode = new SVMNode(j, 0);
nodes.Add(sentenceNode);
}
for (int j = 0; j < sentence1.SentenceWords.Count; j++)
{
CorpusWord cw = Corpus.CorpusList.Find(c => c.Word.Equals(sentence1.SentenceWords[j].Stem.Word) && c.Attribute.Equals(sentence1.SentenceWords[j].Stem.Attribute));
SVMNode sentenceNode = new SVMNode(Corpus.CorpusList.IndexOf(cw), 1);
SVMNode sentenceNodeToRemove = new SVMNode(Corpus.CorpusList.IndexOf(cw), 0);
nodes.Remove(sentenceNodeToRemove);
nodes.Add(sentenceNode);
}
SVMNode[] sentenceNodes = nodes.ToArray();
sentenceNodes = sentenceNodes.OrderBy(x => x.Index).ToArray();
double s1_prediction = SVM.Predict(SVMInterface.model, sentenceNodes);
nodes = new List <SVMNode>();
for (int j = 0; j < Corpus.count; j++)
{
SVMNode sentenceNode = new SVMNode(j, 0);
nodes.Add(sentenceNode);
}
for (int j = 0; j < sentence2.SentenceWords.Count; j++)
{
CorpusWord cw = Corpus.CorpusList.Find(c => c.Word.Equals(sentence2.SentenceWords[j].Stem.Word) && c.Attribute.Equals(sentence2.SentenceWords[j].Stem.Attribute));
SVMNode sentenceNode = new SVMNode(Corpus.CorpusList.IndexOf(cw), 1);
SVMNode sentenceNodeToRemove = new SVMNode(Corpus.CorpusList.IndexOf(cw), 0);
nodes.Remove(sentenceNodeToRemove);
nodes.Add(sentenceNode);
}
sentenceNodes = nodes.ToArray();
sentenceNodes = sentenceNodes.OrderBy(x => x.Index).ToArray();
double s2_prediction = SVM.Predict(SVMInterface.model, sentenceNodes);
Console.WriteLine(s1_prediction + " " + s2_prediction);
if (s1_prediction != s2_prediction)
{
result.Text = "UNRELATED";
}
else if (sentence1 != null && sentence2 != null)
{
if (sentence1.Polarity == sentence2.Polarity)
{
result.Text = "NO CONTRADICTION";
}
if (sentence1.Polarity != sentence2.Polarity)
{
result.Text = "CONTRADICTION";
}
}
}
}
}
