private static void TestCNNBased(string question_filename, string output_filename)
{
StreamReader sr = new StreamReader(question_filename);
CNNBased cb = new CNNBased();
StreamWriter sw = new StreamWriter(output_filename);
Parser parse = new Parser();
while (!sr.EndOfStream)
{
PatternResponse pr = new PatternResponse();
string line = sr.ReadLine();
string question = line.Split(new char[] { '\t' }, StringSplitOptions.RemoveEmptyEntries)[0];
Query query = new Query(question);
parse.PosTagging(ref query);
if (cb.QuestionClassify(query, out pr))
{
sw.Write(question);
sw.Write('\t');
sw.WriteLine(pr.property);
}
else
{
sw.WriteLine(question);
}
}
sw.Flush();
sw.Close();
}
// classify a query based on CNN
public bool QuestionClassify(Query query, out PatternResponse pattern_response)
{
pattern_response = new PatternResponse();
string postagged_query = query.postagged_query;
int tf_class = GetTensorFlowServingResponse(postagged_query);
if (tf_class != -1 && tf_class != 0)
{
pattern_response = new PatternResponse(query, class2pattern[tf_class]);
return(true);
}
return(false);
}
// classify a query based on pattern
public bool QuestionClassify(Query query, out PatternResponse pattern_response)
{
pattern_response = new PatternResponse();
string postagged_query = query.postagged_query;
foreach (Pattern pattern in patterns)
{
Match match = pattern.regex_pattern.Match(postagged_query);
if (match.Success)
{
pattern_response = new PatternResponse(query, pattern);
return(true);
}
}
return(false);
}