我看来NLU没有认识到我提供的完整数据。我在代码中做错了什么，或者错误地假设api应该如何工作？包含来自api的响应，它包含已分析的代码以及完整的提交文本。有一个三角洲，我不确定为什么会这样。
这是我的代码：
def nlu(text):
print("Calling NLU")
url = "https://gateway.watsonplatform.net/natural-language-understanding/api/v1/analyze?version=2017-02-27"
data = {
'text': text,
'language': "en",
'return_analyzed_text': True,
'clean': True,
'features': {
'entities': {
'emotion': True,
'sentiment': True,
'limit': 2
},
"concepts": {
"limit": 15
},
'keywords': {
'emotion': True,
'sentiment': True,
'limit': 2
}
}
}
headers = {
'content-type': "application/json"
}
username = os.getenv("nlu-username")
password = os.getenv("nlu-password")
print("NLU", username, password)
print("data", json.dumps(data))
response = requests.request("POST", url, data=json.dumps(data), headers=headers, auth=(username, password))
print("Done calling NLU")
print(response.text)
这是请求/回复：
"keywords": [
{
"text": "anthropologists study skeletons",
"sentiment": {
"score": 0.0
},"analyzed_text": "move between two thousand eight and two thousand twelve archaeologists excavated the rubble of an ancient hospital in England in the process they uncovered a number of skeletons one in particular belong to a wealthy Mel who lived in the eleventh or twelfth century and died of leprosy between the ages of eighteen and twenty five how do we know all this simply by examining some old soil Kate bones even centuries after death skeletons carry unique features that tell us about their identities and using modern tools and techniques we can read those features as clues this is a branch of science known as biological anthropology it allows researchers to piece together details about Incheon individuals and identify historical events that affected whole populations when researchers uncover a skeleton some of the first clues they gather like age and gender line its morphology which is the structure appearance and size of a skeleton mostly the clavicle stop growing at age twenty five so a skeleton with the clavicle that hasn't fully formed must be younger than similarly the plates in the cranium can continue fusing up to age forty and sometimes beyond by combining these with some microscopic skeletal clues physical anthropologists can estimate an approximate age of death meanwhile pelvic bones reveal gender biologically female palaces are wider allowing women to give birth whereas males are narrower those also betrayed the signs of aging disease disorders like anemia leave their traces on the bones and the condition of teeth can reveal clues to factors like diet and malnutrition which sometimes correlate with wealth or poverty a protein called collagen can give us even more profound details the air we breathe water we drink and food we eat leaves permanent traces in our bones and teeth in the form of chemical compounds these compounds contain measurable quantities called isotopes stable isotopes in bone collagen and tooth enamel varies among mammals dependent on where they lived and what they eat so but analyzing these isotopes we can draw direct inferences regarding the diet and location of historic people not only that but during life bones undergo a constant cycle of remodeling so if someone moves from one place to another bones synthesized after that move will also reflect the new isotopic signatures of the surrounding environment that means that skeletons can be used like migratory maps for instance between one and six fifty A. D. the great city of TOT Makana Mexico bustled with thousands of people researchers examined the isotope ratios and skeletons to the now which held details of their diets when they were young they found evidence for significant migration into the city a majority of the individuals were born elsewhere with further geological and skeletal analysis they may be able to map where those people came from that work in tier two Akon is also an example of how bio anthropologists study skeletons in cemeteries and mass graves and analyze their similarities and differences from not information they can learn about cultural beliefs social norms wars and what caused their deaths today we use these tools to answer big questions about how forces like migration and disease shape the modern world DNA analysis is even possible in some relatively well preserved ancient remains that's helping us understand how diseases like tuberculosis have evolved over the centuries so we can build better treatments for people today ocean skeletons can tell us a surprisingly great deal about the past two of your remains are someday buried intact what might archaeologists of the distant future learn from them"
答案 0 :(得分：0)
我刚试过NLU和你的文字并得到适当的回应。检查以下结果。我认为您应首先使用您的服务凭据尝试Watson API Explorer。它还可以帮助您修复错误的标题或错过的参数到API调用中。
注意：在进行POST调用之前，只需删除参数对象中的＆＃34;元数据＆＃34;：{} ，因为它就是URL和HTML。
{
"semantic_roles": [{
"subject": {
"text": "anthropologists"
},
"sentence": "anthropologists study skeletons",
"object": {
"text": "skeletons"
},
"action": {
"verb": {
"text": "study",
"tense": "present"
},
"text": "study",
"normalized": "study"
}
}],
"language": "en",
"keywords": [{
"text": "anthropologists",
"relevance": 0.966464
},
{
"text": "skeletons",
"relevance": 0.896147
}
],
"entities": [],
"concepts": [{
"text": "Cultural studies",
"relevance": 0.86926,
"dbpedia_resource": "http://dbpedia.org/resource/Cultural_studies"
}],
"categories": [{
"score": 0.927751,
"label": "/science/social science/anthropology"
},
{
"score": 0.219365,
"label": "/education/homework and study tips"
},
{
"score": 0.128377,
"label": "/science"
}
],
"warnings": [
"emotion: cannot locate keyphrase",
"relations: Not Found",
"sentiment: cannot locate keyphrase"
]
}
答案 1 :(得分：0)
在您的代码中
data=json.dumps(data)
将整个JSON对象转换为字符串。那应该只是：
data=data
另外，我建议您使用Python WDC SDK，因为它会让您更轻松。
与上述相同的例子。
import json
from watson_developer_cloud import NaturalLanguageUnderstandingV1
import watson_developer_cloud.natural_language_understanding.features.v1 as Features
username = os.getenv("nlu-username")
password = os.getenv("nlu-password")
nluv1 = NaturalLanguageUnderstandingV1(
username=username,
password=password)
features = [
Features.Entities(),
Features.Concepts(),
Features.Keywords()
]
def nlu(text):
print('Calling NLU')
response = nluv1.analyze(text,features=features, language='en')
print('Done calling NLU')
print(json.dumps(response, indent=2))