Assegnazione tag parole chiave
Quando viene fornito un documento di testo, il servizio di assegnazione tag parole chiave estrae automaticamente parole chiave o frasi chiave che descrivono meglio l'oggetto del documento. Per estrarre le parole chiave, viene utilizzata una combinazione di algoritmi di riconoscimento delle entità denominate (NER) e di tag delle parole chiave non supervisionati.
Nella tabella seguente sono elencate le entità denominate che Content Tagging può identificare:
Formato API
POST /services/v2/predict
Richiesta
La richiesta seguente estrae parole chiave da un documento in base ai parametri di input forniti nel payload.
Per ulteriori informazioni sui parametri di input mostrati, consulta la tabella seguente il payload di esempio.
Questo pdf di esempio file è stato utilizzato nell'esempio riportato in questo documento.
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'
Parametri di input
top_n
min_relevance
min_key_phrase_length
max_key_phrase_length
last_semantic_unit_type
entity_types
Oggetto Document
repo:path
sensei:repoType
sensei:multipart_field_name
dc:format
"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"
Risposta
In caso di esito positivo, la risposta restituisce un oggetto JSON contenente parole chiave estratte nel response
array.
{
[
{
"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
}
]
}