Classifier is a class which is used to classify publications according to their subjects. It depends on the classification of Natural Science Foundation of China(NSFC).
classify(pub_titles, level=0, ntop=5, lang_zh=False, translatation_func=youdao_translate)Use publications' titles to classify which subjects these publications belong to.
A list of strings. The titles of publications.
Classification level(1,2,3), for other numbers you will get all three levels.
NSFC uses a three-level classification. Use graph theory as an example,
A01 mathematics
- A0116 combinatorial mathematics
- A011602 graph theory
The number of possible subjects you want to get.
Whether the titles are Chinese or not. For True, it means you are using Chinese publications.
In fact, the classifier can only work on Chinese words because of the classification standard and the training data. In order to handle publications in other languages, you need to provide a translation function. It should be able to translate a list of strings in another language to Chinese.
In default, we provide a translator based on youdao api. But you cannot use this translator too often because it is only a free version.
A dictionary
'level{x}'(x = 1, 2, 3)':
{
'code': subject code
'name': subject name
'p': probability
}If there are some errors in the method, you will get a {}
nsfc = Classifier()
pub_titles = ['基于多通道卷积神经网络的中文微博情感分析']
subject = nsfc.classify(pub_titles)subject:
{
'level1': [
{'code': 'F02', 'name': '计算机科学', 'p': 0.9745969772338867},
{'code': 'F01', 'name': '电子学与信息系统', 'p': 0.02385014481842518},
{'code': 'B05', 'name': '分析化学', 'p': 0.0005464374553412199},
{'code': 'F03', 'name': '自动化', 'p': 0.00039022043347358704},
{'code': 'H18', 'name': '影像医学与生物医学工程', 'p': 0.0001973187318071723}
],
'level2': [
{'code': 'F0206', 'name': '自然语言理解与机器翻译', 'p': 0.8545559048652649},
{'code': 'F0205', 'name': '计算机应用技术', 'p': 0.08089018613100052},
{'code': 'F0305', 'name': '人工智能与知识工程', 'p': 0.023599255830049515},
{'code': 'B0512', 'name': '化学计量学与化学信息学', 'p': 0.0228357}
],
'level3': [
{'code': 'F020601', 'name': '计算语言学', 'p': 0.9999170303344727},
{'code': 'F020504', 'name': '生物信息计算', 'p': 4.625070505426265e-05},
{'code': 'F020506', 'name': '人机界面技术', 'p': 2.3111495465855114e-05},
{'code': 'F010403', 'name': '物联网', 'p': 2.2251791961025447e-05},
{'code': 'F010303', 'name': '协作通信', 'p': 2.0015930203953758e-05}
]
}
An online version of the method classify
A list of strings. The titles of publications.
In the Response object, there will be three fields.
0: Success
1: There are some errors.
success: Success
If there are some errors, you will get the error information.
The return value from the method.
POST /tools/v1/predict/nsfc? HTTP/1.1
Host: innovaapi.aminer.cn
Content-Type: application/json
User-Agent: PostmanRuntime/7.13.0
Accept: */*
Cache-Control: no-cache
Postman-Token: 5f0fbe87-e333-40b1-b9c3-23f64c137c15,1927af8e-4a86-4319-8024-684d6b9e46f7
Host: innovaapi.aminer.cn
accept-encoding: gzip, deflate
content-length: 100
Connection: keep-alive
cache-control: no-cache
{
"titles": [
"基于多通道卷积神经网络的中文微博情感分析"
]
}Return Message:
{
"status": 0,
"message": "success",
"data": {
"level1": [
{
"code": "F02",
"name": "计算机科学",
"p": 0.9745969772338867
},
{
"code": "F01",
"name": "电子学与信息系统",
"p": 0.02385014481842518
},
{
"code": "B05",
"name": "分析化学",
"p": 0.0005464374553412199
},
{
"code": "F03",
"name": "自动化",
"p": 0.00039022043347358704
},
{
"code": "H18",
"name": "影像医学与生物医学工程",
"p": 0.0001973187318071723
}
],
"level2": [
{
"code": "F0206",
"name": "自然语言理解与机器翻译",
"p": 0.8545559048652649
},
{
"code": "F0205",
"name": "计算机应用技术",
"p": 0.08089018613100052
},
{
"code": "F0305",
"name": "人工智能与知识工程",
"p": 0.023599255830049515
},
{
"code": "B0512",
"name": "化学计量学与化学信息学",
"p": 0.022835755720734596
},
{
"code": "F0104",
"name": "通信网络",
"p": 0.01253295037895441
}
],
"level3": [
{
"code": "F020601",
"name": "计算语言学",
"p": 0.9999170303344727
},
{
"code": "F020504",
"name": "生物信息计算",
"p": 0.00004625070505426265
},
{
"code": "F020506",
"name": "人机界面技术",
"p": 0.000023111495465855114
},
{
"code": "F010403",
"name": "物联网",
"p": 0.000022251791961025447
},
{
"code": "F010303",
"name": "协作通信",
"p": 0.000020015930203953758
}
]
}
}
Get a professor's research interests according to his publications' titles.
the professor's id in AMiner.
For example, you want to know Qiang Yang's research interests. First, you should search Qiang Yang in AMiner, and get his page url https://www.aminer.cn/profile/qiang-yang/53f48041dabfae963d25910a. His id in AMiner is the suffix of the url string 53f48041dabfae963d25910a.
In the Response object, there will be three fields.
0: Success
1: There are some errors.
success: Success
If there are some errors, you will get the error information.
The return value from the method.
https://innovaapi.aminer.cn/tools/v1/predict/nsfc/person?pid=53f48041dabfae963d25910a
| level | top1 | top5 |
|---|---|---|
| 1 | 0.5079 | 0.8331 |
| 2 | 0.3629 | 0.6668 |
| 3 | 0.3342 | 0.6317 |