WatsonException: Error: unsupported text language, Code: 400
WatsonException: Error: unsupported text language, Code: 400
我正在使用自然语言理解 api。
我正在使用 'hmmmm nawa ohh wen am I gona win ds tin' 的文本,但出现错误
WatsonException: Error: unsupported text language, Code: 400
我的代码是:
response = natural_language_understanding.analyze(
text='hmmmm nawa ohh wen am I gona win ds tin',
features=[features.Sentiment(), features.Keywords(), features.Emotion(), features.Categories()])
如何将这类文本传递给 NLU api。
需要帮助。
它失败了,因为在尝试确定特征之前,它会尝试猜测语言是什么。设置语言将防止这种情况。
例如:
question = 'hmmmm nawa ohh wen am I gona win ds tin'
f = [
features.Categories(),
features.Concepts(),
features.Emotion(),
features.Entities(),
features.Relations(),
features.SemanticRoles(),
features.Sentiment()
]
r = nlu.analyze(text=question, features=f, language='en')
print(json.dumps(r, indent=2))
输出这个:
{
"sentiment": {
"document": {
"score": 0.0,
"label": "neutral"
}
},
"semantic_roles": [
{
"subject": {
"text": "I"
},
"sentence": "hmmmm nawa ohh wen am I gona win ds tin",
"object": {
"text": "ds tin"
},
"action": {
"verb": {
"text": "win",
"tense": "present"
},
"text": "win",
"normalized": "win"
}
}
],
"relations": [],
"language": "en",
"entities": [],
"emotion": {
"document": {
"emotion": {
"sadness": 0.193275,
"joy": 0.309168,
"fear": 0.167981,
"disgust": 0.06316,
"anger": 0.130959
}
}
},
"concepts": [],
"categories": [
{
"score": 0.899547,
"label": "/art and entertainment"
},
{
"score": 0.365657,
"label": "/hobbies and interests/reading"
},
{
"score": 0.189432,
"label": "/art and entertainment/movies and tv/movies"
}
]
}
虽然这不是正确的英语,所以我不希望结果很好。
您可以在此处查看支持的语言功能:
我正在使用自然语言理解 api。 我正在使用 'hmmmm nawa ohh wen am I gona win ds tin' 的文本,但出现错误
WatsonException: Error: unsupported text language, Code: 400
我的代码是:
response = natural_language_understanding.analyze(
text='hmmmm nawa ohh wen am I gona win ds tin',
features=[features.Sentiment(), features.Keywords(), features.Emotion(), features.Categories()])
如何将这类文本传递给 NLU api。 需要帮助。
它失败了,因为在尝试确定特征之前,它会尝试猜测语言是什么。设置语言将防止这种情况。
例如:
question = 'hmmmm nawa ohh wen am I gona win ds tin'
f = [
features.Categories(),
features.Concepts(),
features.Emotion(),
features.Entities(),
features.Relations(),
features.SemanticRoles(),
features.Sentiment()
]
r = nlu.analyze(text=question, features=f, language='en')
print(json.dumps(r, indent=2))
输出这个:
{
"sentiment": {
"document": {
"score": 0.0,
"label": "neutral"
}
},
"semantic_roles": [
{
"subject": {
"text": "I"
},
"sentence": "hmmmm nawa ohh wen am I gona win ds tin",
"object": {
"text": "ds tin"
},
"action": {
"verb": {
"text": "win",
"tense": "present"
},
"text": "win",
"normalized": "win"
}
}
],
"relations": [],
"language": "en",
"entities": [],
"emotion": {
"document": {
"emotion": {
"sadness": 0.193275,
"joy": 0.309168,
"fear": 0.167981,
"disgust": 0.06316,
"anger": 0.130959
}
}
},
"concepts": [],
"categories": [
{
"score": 0.899547,
"label": "/art and entertainment"
},
{
"score": 0.365657,
"label": "/hobbies and interests/reading"
},
{
"score": 0.189432,
"label": "/art and entertainment/movies and tv/movies"
}
]
}
虽然这不是正确的英语,所以我不希望结果很好。
您可以在此处查看支持的语言功能: