关键词标记
当给定一个文本文档时，关键字标记服务自动提取最能描述文档主题的关键字或关键短语。 为了提取关键词，使用命名实体识别(NER)和无监督关键词标记算法相结合。
下表列出了满足以下条件的命名实体 Content Tagging 可以识别：
实体名称
描述
人员
人，包括虚构的人。
GPE
国家/地区、城市和州。
LOC
非GPE位置、山脉和水体。
常见问题解答
建筑、机场、高速公路、桥梁等。
组织
公司、代理商、机构等
产品
物品、车辆、食品等。 （不是服务。）
事件
命名为飓风、战斗、战争、体育赛事等。
艺术作品
书籍、歌曲等标题。
法律
被列为法律的文件。
语言
任何命名语言。
API格式
POST /services/v2/predict
请求
以下请求基于有效负荷中提供的输入参数从文档中提取关键词。
有关显示的输入参数的更多信息，请参阅示例有效负载下表。
此 示例pdf 在本文档所示的示例中使用了文件。
curl -w'\n' -i -X POST https://sensei.adobe.io/services/v2/predict \
-H 'Prefer: respond-async, wait=59' \
-H "x-api-key: $API_KEY" \
-H "content-type: multipart/form-data" \
-H "authorization: Bearer $API_TOKEN" \
-F 'contentAnalyzerRequests={
"sensei:name": "test",
"sensei:invocation_mode": "synchronous",
"sensei:invocation_batch": false,
"sensei:engines": [
{
"sensei:execution_info": {
"sensei:engine": "Feature:cintel-ner:Service-1e9081c865214d1e8bace51dd918b5c0"
},
"sensei:inputs": {
"documents": [
{
"sensei:multipart_field_name": "infile_1",
"dc:format": "application/pdf"
}
]
},
"sensei:params": {
"application-id": "1234",
"min_key_phrase_length": 1,
"max_key_phrase_length": 3,
"top_n": 5,
"last_semantic_unit_type": "concept"
},
"sensei:outputs":{
"result" : {
"sensei:multipart_field_name" : "result",
"dc:format": "application/json"
}
}
}
]
}' \
-F 'infile_1=@simple-text.pdf'
输入参数
属性
描述
必需
top_n要返回的结果数。 0，返回所有结果。 当与阈值一起使用时，返回的结果数将少于任一限制。
否
min_relevance分数阈值，必须低于该阈值返回结果。 排除参数可返回所有结果。
否
min_key_phrase_length关键短语中所需的最小字数。
否
max_key_phrase_length关键短语中所需的最大字数。
否
last_semantic_unit_type仅返回分层响应中最高到给定级别的语义单位。 “key_phrase”仅返回关键短语，“linked_entity”仅返回关键短语及其对应的链接实体，“concept”返回关键短语、链接实体和概念。
否
entity_types要作为关键短语返回的实体类型。
否
文档对象
名称
数据类型
必需
默认
值
描述
repo:path字符串
-
-
-
要从中提取关键短语的文档的预签名URL。
sensei:repoType字符串
-
-
HTTPS
存储文档的存储库类型。
sensei:multipart_field_name字符串
-
-
-
将文档作为多部分参数传递时，请使用此选项，而不是使用预签名URL。
dc:format字符串
是
-
"text/plain"，
"application/pdf"，
"text/pdf"，
"text/html"，
"text/rtf"，
"application/rtf"，
“application/msword”，
"application/vnd.openxmlformats-officedocument.wordprocessingml.document"，
"application/mspowerpoint"，
"application/vnd.ms-powerpoint"，
"application/vnd.openxmlformats-officedocument.presentationml.presentation"
"application/pdf"，
"text/pdf"，
"text/html"，
"text/rtf"，
"application/rtf"，
“application/msword”，
"application/vnd.openxmlformats-officedocument.wordprocessingml.document"，
"application/mspowerpoint"，
"application/vnd.ms-powerpoint"，
"application/vnd.openxmlformats-officedocument.presentationml.presentation"
在处理之前，将根据允许的输入编码类型检查文档编码。
响应
成功的响应会返回一个JSON对象，其中包含提取的关键字，该对象位于 response 数组。
{
[
{
"key_phrases": [
{
"name": "Canada",
"type": "GPE",
"relevance": 0.9525035277863068,
"confidence": 1.0,
"linked_entity": {
"name": "Canada",
"id": "b27a82e6-e963-45de-add8-dc4f3f0dd399",
"confidence": 1.0,
"relevance": 0.9706433035237365,
"concepts": [
{
"name": "Commonwealth realm",
"relationship": "instance_of",
"id": "f5354ab6-ad25-406a-b289-9209db0db8ea",
"confidence": 1.0,
"relevance": 0.9525035277863066
},
{
"name": "sovereign state",
"relationship": "instance_of",
"id": "10c24191-beef-43cc-a823-c170f217fe12",
"confidence": 1.0,
"relevance": 0.9525035277863066
},
{
"name": "dominion of the British Empire",
"relationship": "instance_of",
"id": "4ffabaee-e6ab-422d-b121-145dcdbcf427",
"confidence": 1.0,
"relevance": 0.9525035277863066
},
{
"name": "country",
"relationship": "instance_of",
"id": "6e8f43cb-7e64-41fc-93b4-119adfe87926",
"confidence": 1.0,
"relevance": 0.9525035277863066
},
{
"name": "North America",
"relationship": "part_of",
"id": "0f4b1f78-9681-414a-91c6-576ed643941a",
"confidence": 1.0,
"relevance": 0.9525035277863066
}
]
}
},
{
"name": "Sherlock Homles",
"type": "ENTITY_UNKNOWN_TYPE",
"relevance": 0.9516463011782174,
"confidence": 1.0,
"linked_entity": null
},
{
"name": "Albert Einstein",
"type": "PERSON",
"relevance": 0.95080732382989,
"confidence": 1.0,
"linked_entity": {
"name": "Albert Einstein",
"id": "0fdb37f6-f575-4b4d-91e9-fbff57eae0ab",
"confidence": 1.0,
"relevance": 0.9695742180192723,
"concepts": [
{
"name": "pedagogue",
"relationship": "occupation",
"id": "1439eb14-2988-43cc-865d-ad5a60d3ea62",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "philosopher of science",
"relationship": "occupation",
"id": "eefb9bbf-e617-4434-abb2-56b5853abd3a",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "university teacher",
"relationship": "occupation",
"id": "bb2c4745-4116-46ef-a122-c28c2f902026",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "science writer",
"relationship": "occupation",
"id": "5084431d-9073-45cb-be82-4a6898becd5b",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "non-fiction writer",
"relationship": "occupation",
"id": "57cc1f7b-5391-458b-9303-ec35b3ba01a4",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "patent examiner",
"relationship": "occupation",
"id": "d3f10fc5-ca81-4049-8c48-3d935552d9e7",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "philosopher",
"relationship": "occupation",
"id": "04d3cd32-68ad-4b71-9231-bdf3acfb09b2",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "scientist",
"relationship": "occupation",
"id": "dc8c068b-aa75-4ece-acd7-06fa304964fb",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "physicist",
"relationship": "occupation",
"id": "56ac942c-12a2-42c1-b10c-d1394a7971af",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "teacher",
"relationship": "occupation",
"id": "c70301bd-bcf4-47ab-b958-b983f0b0a6bd",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "human",
"relationship": "instance_of",
"id": "ead8a1d7-f901-44e6-b80f-63ebbbca4ffe",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "professor",
"relationship": "occupation",
"id": "c6d691f2-1e26-49fd-8481-58cb2d64d3e9",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "mathematician",
"relationship": "occupation",
"id": "23bf46db-a69a-4546-b18a-690a41144caa",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "theoretical physics",
"relationship": "field_of_work",
"id": "d6c03027-4efd-49d6-a7e5-ac4994c9143e",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "theoretical physicist",
"relationship": "occupation",
"id": "eedb6531-c2bf-4d05-af92-6f21751bc894",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "inventor",
"relationship": "occupation",
"id": "7baf322e-5913-4e2a-997a-90a039b0ff5c",
"confidence": 1.0,
"relevance": 0.9508073238298899
},
{
"name": "writer",
"relationship": "occupation",
"id": "4c4c287c-0d83-4da3-b8c7-26df5adc9b33",
"confidence": 1.0,
"relevance": 0.9508073238298899
}
]
}
},
{
"name": "Toronto",
"type": "GPE",
"relevance": 0.9370046727951885,
"confidence": 1.0,
"linked_entity": {
"name": "Toronto",
"id": "762db630-b272-4828-b1af-e7c65334e1d3",
"confidence": 1.0,
"relevance": 0.9608202651283239,
"concepts": [
{
"name": "provincial or territorial capital city in Canada",
"relationship": "instance_of",
"id": "d7447629-e940-43b1-a726-4ac3f675410c",
"confidence": 1.0,
"relevance": 0.9370046727951883
},
{
"name": "city",
"relationship": "instance_of",
"id": "d9d95c34-a2ce-4098-bd9d-3616b85620a8",
"confidence": 1.0,
"relevance": 0.9370046727951883
},
{
"name": "big city",
"relationship": "instance_of",
"id": "68275742-3451-40af-8f5a-84211953a438",
"confidence": 1.0,
"relevance": 0.9370046727951883
},
{
"name": "single-tier municipality",
"relationship": "instance_of",
"id": "a0f67ef3-52bb-44d9-bc52-9059d37c6d0c",
"confidence": 1.0,
"relevance": 0.9370046727951883
},
{
"name": "city with millions of inhabitants",
"relationship": "instance_of",
"id": "b08def76-4b71-4545-9efb-f4858aaf253d",
"confidence": 1.0,
"relevance": 0.9370046727951883
}
]
}
},
{
"name": "vacation",
"type": "KEY_PHRASE",
"relevance": 0.933964522339908,
"confidence": 1.0,
"linked_entity": null
}
],
"detected_languages": [
{
"language": "en",
"confidence": 0.9999951616458576
}
],
"word_count": 183
}
]
}
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