private async Task OnNamedEntityRecognize()
{
try
{
resBosonNamedEntity = await BosonAIHelper.NamedEntityRecognize(textInput.Text.Trim());
if (resBosonNamedEntity.entity.Count > 0)
{
for (int i = 0; i <= resBosonNamedEntity.entity.Count - 1; i++)
{
int nStart = Convert.ToInt32(resBosonNamedEntity.entity[i][0]);
int nEnd = Convert.ToInt32(resBosonNamedEntity.entity[i][1]);
List <string> list = resBosonNamedEntity.word.Skip(nStart).Take(nEnd - nStart).ToList();
string strEntity = "";
foreach (var item in list)
{
strEntity += item;
}
NLPWord nlp = new NLPWord
{
word = strEntity,
width = strEntity.Length * 20,
bgcolor = NameEntityHelper.GetNameEntityColor_Boson(resBosonNamedEntity.entity[i][2])
};
NamedEntityItems.Add(nlp);
}
}
}
catch { }
}
private async Task GetTencentAIResponse()
{
try
{
resTencentPosTag = await TencentAIHelper.WordPositionTagging(textInput.Text.Trim());
if (resTencentPosTag != null && resTencentPosTag.ret == 0 && resTencentPosTag.msg == "ok")
{
SegTagItems.Clear();
listBaseTokens = resTencentPosTag.data.base_tokens;
listMixTokens = resTencentPosTag.data.mix_tokens;
}
resTencentProperNouns = await TencentAIHelper.WordProperNouns(textInput.Text.Trim());
if (resTencentProperNouns != null && resTencentProperNouns.ret == 0 && resTencentProperNouns.msg == "ok")
{
NamedEntityItems.Clear();
foreach (var item in resTencentProperNouns.data.ner_tokens)
{
NLPWord nlp = new NLPWord
{
word = item.word,
width = item.length * 20,
bgcolor = NameEntityHelper.GetNameEntityColor_Tencent(item.types[0].ToString())
};
NamedEntityItems.Add(nlp);
}
}
resTencentSynonym = await TencentAIHelper.WordSynonym(textInput.Text.Trim());
if (resTencentSynonym != null && resTencentSynonym.ret == 0 && resTencentSynonym.msg == "ok")
{
SuggestItems.Clear();
foreach (var item in resTencentSynonym.data.syn_tokens)
{
NLPWord nlp = new NLPWord
{
word = item.ori_word.word,
width = item.ori_word.length * 20,
bgcolor = "#9acd32"
};
SuggestItems.Add(nlp);
foreach (var iitem in item.syn_words)
{
string strWeight = (Convert.ToInt32(iitem.weight * 100)).ToString();
NLPWord inlp = new NLPWord
{
word = iitem.word + "(" + strWeight + ")",
//width = item.ori_word.length * 20,
bgcolor = "#99CC67"
};
SuggestItems.Add(inlp);
}
}
}
resTencentComponent = await TencentAIHelper.WordComponent(textInput.Text.Trim());
if (resTencentComponent != null && resTencentComponent.ret == 0 && resTencentComponent.msg == "ok")
{
sliderTencentClassify.Value = resTencentComponent.data.intent + 0.5;
}
else if (resTencentComponent != null && resTencentComponent.ret == 16393)
{
sliderTencentClassify.Value = 0.5;
}
resTencentEmotionPolar = await TencentAIHelper.WordEmotionPolar(textInput.Text.Trim());
if (resTencentEmotionPolar != null && resTencentEmotionPolar.ret == 0 && resTencentEmotionPolar.msg == "ok")
{
double positive = 0.0, negtive = 0.0;
if (resTencentEmotionPolar.data.polar == 1)
{
positive = 1;
negtive = 0;
}
else if (resTencentEmotionPolar.data.polar == -1)
{
positive = 0;
negtive = 1;
}
else if (resTencentEmotionPolar.data.polar == 0)
{
positive = 1;
negtive = 1;
}
if ((Application.Current as App).strCurrentLanguage.ToLower().Equals("zh-cn"))
{
// Create a new chart data point for each value you want in the PieSeries
var sliceOne = new ChartDataModel {
Value = positive, Title = "正面"
};
var sliceTwo = new ChartDataModel {
Value = negtive, Title = "负面"
};
// Add those items to the list
chartItems.Add(sliceOne);
chartItems.Add(sliceTwo);
}
else
{
// Create a new chart data point for each value you want in the PieSeries
var sliceOne = new ChartDataModel {
Value = positive, Title = "Positive"
};
var sliceTwo = new ChartDataModel {
Value = negtive, Title = "Negtive"
};
// Add those items to the list
chartItems.Add(sliceOne);
chartItems.Add(sliceTwo);
}
MyDoughnutSeries.ItemsSource = chartItems;
}
TipServices.TipRecognizeSucceed();
}
catch
{
if (resTencentPosTag.ret < 0)
{
TipServices.TipSystemError();
}
else
{
TipServices.TipRecognizeFailed();
}
}
}