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A analisar entidades

A análise de entidades inspeciona o texto fornecido para encontrar entidades conhecidas (nomes próprios, como figuras públicas, pontos de referência, etc.) e devolve informações sobre essas entidades. A análise de entidades é realizada com o método analyzeEntities. Para obter informações sobre os tipos de entidades que a linguagem natural identifica, consulte a documentação Entity. Para obter informações sobre os idiomas suportados pela API Natural Language, consulte o artigo Suporte de idiomas.

Esta secção demonstra algumas formas de detetar entidades num documento. Para cada documento, tem de enviar um pedido separado.

Analisar entidades numa string

Segue-se um exemplo de como realizar uma análise de entidades numa string de texto enviada diretamente para a API Natural Language:

Protocolo

Para analisar entidades num documento, faça um pedido POST ao método REST documents:analyzeEntities e forneça o corpo do pedido adequado, conforme mostrado no exemplo seguinte.

O exemplo usa o comando gcloud auth application-default print-access-token para obter um token de acesso para uma conta de serviço configurada para o projeto usando a CLI gcloud da Google Cloud Platform. Para obter instruções sobre a instalação da CLI gcloud e a configuração de um projeto com uma conta de serviço, consulte o guia de início rápido.

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'encodingType': 'UTF8',
  'document': {
    'type': 'PLAIN_TEXT',
    'content': 'President Trump will speak from the White House, located
  at 1600 Pennsylvania Ave NW, Washington, DC, on October 7.'
  }
}" "https://language.googleapis.com/v2/documents:analyzeEntities"

Se não especificar document.language_code, o idioma é detetado automaticamente. Para informações sobre os idiomas suportados pela API Natural Language, consulte o artigo Suporte de idiomas. Consulte a Document documentação de referência para obter mais informações sobre a configuração do corpo do pedido.

Se o pedido for bem-sucedido, o servidor devolve um código de estado HTTP 200 OK e a resposta no formato JSON:

{
  "entities": [
    {
      "name": "October 7",
      "type": "DATE",
      "metadata": {
        "month": "10",
        "day": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "October 7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600",
      "type": "NUMBER",
      "metadata": {
        "value": "1600"
      },
      "mentions": [
        {
          "text": {
            "content": "1600",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "7",
      "type": "NUMBER",
      "metadata": {
        "value": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW, Washington, DC",
      "type": "ADDRESS",
      "metadata": {
        "locality": "Washington",
        "narrow_region": "District of Columbia",
        "street_name": "Pennsylvania Avenue Northwest",
        "street_number": "1600",
        "broad_region": "District of Columbia",
        "country": "US"
      },
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW, Washington, DC",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.901
        }
      ]
    },
    {
      "name": "President",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "President",
            "beginOffset": -1
          },
          "type": "COMMON",
          "probability": 0.941
        }
      ]
    },
    {
      "name": "Trump",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Trump",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.948
        }
      ]
    },
    {
      "name": "Washington, DC",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Washington, DC",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.92
        }
      ]
    },
    {
      "name": "White House",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "White House",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.785
        }
      ]
    }
  ],
  "languageCode": "en",
  "languageSupported": true
}

A matriz entities contém Entity objetos que representam as entidades detetadas, que incluem informações como o nome e o tipo da entidade.

gcloud

Consulte o comando analyze-entities para ver os detalhes completos.

Para realizar a análise de entidades, use a CLI gcloud e use a flag --content para identificar o conteúdo a analisar:

gcloud ml language analyze-entities --content="President Trump will speak from the White House, located
  at 1600 Pennsylvania Ave NW, Washington, DC, on October 7."

Se o pedido for bem-sucedido, o servidor devolve uma resposta no formato JSON:

{
  "entities": [
    {
      "name": "Trump",
      "type": "PERSON",
      "metadata": {
        "mid": "/m/0cqt90",
        "wikipedia_url": "https://en.wikipedia.org/wiki/Donald_Trump"
      },
      "salience": 0.7936003,
      "mentions": [
        {
          "text": {
            "content": "Trump",
            "beginOffset": 10
          },
          "type": "PROPER"
        },
        {
          "text": {
            "content": "President",
            "beginOffset": 0
          },
          "type": "COMMON"
        }
      ]
    },
    {
      "name": "White House",
      "type": "LOCATION",
      "metadata": {
        "mid": "/m/081sq",
        "wikipedia_url": "https://en.wikipedia.org/wiki/White_House"
      },
      "salience": 0.09172433,
      "mentions": [
        {
          "text": {
            "content": "White House",
            "beginOffset": 36
          },
          "type": "PROPER"
        }
      ]
    },
    {
      "name": "Pennsylvania Ave NW",
      "type": "LOCATION",
      "metadata": {
        "mid": "/g/1tgb87cq"
      },
      "salience": 0.085507184,
      "mentions": [
        {
          "text": {
            "content": "Pennsylvania Ave NW",
            "beginOffset": 65
          },
          "type": "PROPER"
        }
      ]
    },
    {
      "name": "Washington, DC",
      "type": "LOCATION",
      "metadata": {
        "mid": "/m/0rh6k",
        "wikipedia_url": "https://en.wikipedia.org/wiki/Washington,_D.C."
      },
      "salience": 0.029168168,
      "mentions": [
        {
          "text": {
            "content": "Washington, DC",
            "beginOffset": 86
          },
          "type": "PROPER"
        }
      ]
    }
    {
      "name": "1600 Pennsylvania Ave NW, Washington, DC",
      "type": "ADDRESS",
      "metadata": {
        "country": "US",
        "sublocality": "Fort Lesley J. McNair",
        "locality": "Washington",
        "street_name": "Pennsylvania Avenue Northwest",
        "broad_region": "District of Columbia",
        "narrow_region": "District of Columbia",
        "street_number": "1600"
      },
      "salience": 0,
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW, Washington, DC",
            "beginOffset": 60
          },
          "type": "TYPE_UNKNOWN"
        }
      ]
      }
    }
    {
      "name": "1600",
       "type": "NUMBER",
       "metadata": {
           "value": "1600"
       },
       "salience": 0,
       "mentions": [
         {
          "text": {
              "content": "1600",
              "beginOffset": 60
           },
           "type": "TYPE_UNKNOWN"
        }
     ]
     },
     {
       "name": "October 7",
       "type": "DATE",
       "metadata": {
         "day": "7",
         "month": "10"
       },
       "salience": 0,
       "mentions": [
         {
           "text": {
             "content": "October 7",
             "beginOffset": 105
            },
           "type": "TYPE_UNKNOWN"
         }
       ]
     }
     {
       "name": "7",
       "type": "NUMBER",
       "metadata": {
         "value": "7"
       },
       "salience": 0,
       "mentions": [
         {
           "text": {
             "content": "7",
             "beginOffset": 113
           },
         "type": "TYPE_UNKNOWN"
         }
        ]
     }
  ],
  "language": "en"
}

A matriz entities contém Entity objetos que representam as entidades detetadas, que incluem informações como o nome e o tipo da entidade.

Go

Para saber como instalar e usar a biblioteca cliente da API Natural Language, consulte o artigo Bibliotecas cliente da API Natural Language. Para mais informações, consulte a documentação de referência da API Go de linguagem natural.

Para se autenticar na API Natural Language, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

import (
	"context"
	"fmt"
	"io"

	language "cloud.google.com/go/language/apiv2"
	"cloud.google.com/go/language/apiv2/languagepb"
)

// analyzeEntities sends a string of text to the Cloud Natural Language API to
// detect the entities of the text.
func analyzeEntities(w io.Writer, text string) error {
	ctx := context.Background()

	// Initialize client.
	client, err := language.NewClient(ctx)
	if err != nil {
		return err
	}
	defer client.Close()

	resp, err := client.AnalyzeEntities(ctx, &languagepb.AnalyzeEntitiesRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
		EncodingType: languagepb.EncodingType_UTF8,
	})

	if err != nil {
		return fmt.Errorf("AnalyzeEntities: %w", err)
	}
	fmt.Fprintf(w, "Response: %q\n", resp)

	return nil
}

Java

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();
  AnalyzeEntitiesRequest request =
      AnalyzeEntitiesRequest.newBuilder()
          .setDocument(doc)
          .setEncodingType(EncodingType.UTF16)
          .build();

  AnalyzeEntitiesResponse response = language.analyzeEntities(request);

  // Print the response
  for (Entity entity : response.getEntitiesList()) {
    System.out.printf("Entity: %s", entity.getName());
    System.out.println("Metadata: ");
    for (Map.Entry<String, String> entry : entity.getMetadataMap().entrySet()) {
      System.out.printf("%s : %s", entry.getKey(), entry.getValue());
    }
    for (EntityMention mention : entity.getMentionsList()) {
      System.out.printf("Begin offset: %d\n", mention.getText().getBeginOffset());
      System.out.printf("Content: %s\n", mention.getText().getContent());
      System.out.printf("Type: %s\n\n", mention.getType());
      System.out.printf("Probability: %s\n\n", mention.getProbability());
    }
  }
}

Node.js

// Imports the Google Cloud client library
const language = require('@google-cloud/language').v2;

// Creates a client
const client = new language.LanguageServiceClient();

/**
 * TODO(developer): Uncomment the following line to run this code.
 */
// const text = 'Your text to analyze, e.g. Hello, world!';

// Prepares a document, representing the provided text
const document = {
  content: text,
  type: 'PLAIN_TEXT',
};

// Detects entities in the document
const [result] = await client.analyzeEntities({document});

const entities = result.entities;

console.log('Entities:');
entities.forEach(entity => {
  console.log(entity.name);
  console.log(` - Type: ${entity.type}`);
  if (entity.metadata) {
    console.log(` - Metadata: ${entity.metadata}`);
  }
});

Python

from google.cloud import language_v2


def sample_analyze_entities(text_content: str = "California is a state.") -> None:
    """
    Analyzes Entities in a string.

    Args:
      text_content: The text content to analyze
    """

    client = language_v2.LanguageServiceClient()

    # Available types: PLAIN_TEXT, HTML
    document_type_in_plain_text = language_v2.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    # https://cloud.google.com/natural-language/docs/languages
    language_code = "en"
    document = {
        "content": text_content,
        "type_": document_type_in_plain_text,
        "language_code": language_code,
    }

    # Available values: NONE, UTF8, UTF16, UTF32.
    # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType.
    encoding_type = language_v2.EncodingType.UTF8

    response = client.analyze_entities(
        request={"document": document, "encoding_type": encoding_type}
    )

    for entity in response.entities:
        print(f"Representative name for the entity: {entity.name}")

        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al.
        # See https://cloud.google.com/natural-language/docs/reference/rest/v2/Entity#type.
        print(f"Entity type: {language_v2.Entity.Type(entity.type_).name}")

        # Loop over the metadata associated with entity.
        # Some entity types may have additional metadata, e.g. ADDRESS entities
        # may have metadata for the address street_name, postal_code, et al.
        for metadata_name, metadata_value in entity.metadata.items():
            print(f"{metadata_name}: {metadata_value}")

        # Loop over the mentions of this entity in the input document.
        # The API currently supports proper noun mentions.
        for mention in entity.mentions:
            print(f"Mention text: {mention.text.content}")

            # Get the mention type, e.g. PROPER for proper noun
            print(f"Mention type: {language_v2.EntityMention.Type(mention.type_).name}")

            # Get the probability score associated with the first mention of the entity in the (0, 1.0] range.
            print(f"Probability score: {mention.probability}")

    # Get the language of the text, which will be the same as
    # the language specified in the request or, if not specified,
    # the automatically-detected language.
    print(f"Language of the text: {response.language_code}")

Idiomas adicionais

C#: Siga as instruções de configuração do C# na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para .NET.

PHP: Siga as instruções de configuração do PHP na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para PHP.

Ruby: Siga as instruções de configuração do Ruby na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para Ruby.

Analisar entidades a partir do Cloud Storage

Para sua conveniência, a API Natural Language pode realizar a análise de entidades diretamente num ficheiro localizado no Cloud Storage, sem necessidade de enviar o conteúdo do ficheiro no corpo do seu pedido.

Segue-se um exemplo de como realizar uma análise de entidades num ficheiro localizado no Cloud Storage.

Protocolo

Para analisar entidades de um documento armazenado no Cloud Storage, faça um pedido POST ao método REST documents:analyzeEntities e forneça o corpo do pedido adequado com o caminho para o documento, conforme mostrado no exemplo seguinte.

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'document':{
    'type':'PLAIN_TEXT',
    'gcsContentUri':'gs://<bucket-name>/<object-name>'
  }
}" "https://language.googleapis.com/v2/documents:analyzeEntities"

Se o pedido for bem-sucedido, o servidor devolve um código de estado HTTP 200 OK e a resposta no formato JSON:

{
  "entities": [
    {
      "name": "October 7",
      "type": "DATE",
      "metadata": {
        "month": "10",
        "day": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "October 7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600",
      "type": "NUMBER",
      "metadata": {
        "value": "1600"
      },
      "mentions": [
        {
          "text": {
            "content": "1600",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "7",
      "type": "NUMBER",
      "metadata": {
        "value": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW, Washington, DC",
      "type": "ADDRESS",
      "metadata": {
        "locality": "Washington",
        "narrow_region": "District of Columbia",
        "street_name": "Pennsylvania Avenue Northwest",
        "street_number": "1600",
        "broad_region": "District of Columbia",
        "country": "US"
      },
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW, Washington, DC",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.901
        }
      ]
    },
    {
      "name": "President",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "President",
            "beginOffset": -1
          },
          "type": "COMMON",
          "probability": 0.941
        }
      ]
    },
    {
      "name": "Trump",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Trump",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.948
        }
      ]
    },
    {
      "name": "Washington, DC",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Washington, DC",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.92
        }
      ]
    },
    {
      "name": "White House",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "White House",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.785
        }
      ]
    }
  ],
  "languageCode": "en",
  "languageSupported": true
}

A matriz entities contém Entity objetos que representam as entidades detetadas, que incluem informações como o nome e o tipo da entidade.

gcloud

Consulte o comando analyze-entities para ver os detalhes completos.

Para realizar a análise de entidades num ficheiro no Cloud Storage, use a ferramenta de linha de comandos e a flag --content-file para identificar o caminho do ficheiro que contém o conteúdo a analisar:gcloud

gcloud ml language analyze-entities --content-file=gs://YOUR_BUCKET_NAME/YOUR_FILE_NAME

Se o pedido for bem-sucedido, o servidor devolve uma resposta no formato JSON:

{
  "entities": [
    {
      "name": "October 7",
      "type": "DATE",
      "metadata": {
        "month": "10",
        "day": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "October 7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600",
      "type": "NUMBER",
      "metadata": {
        "value": "1600"
      },
      "mentions": [
        {
          "text": {
            "content": "1600",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "7",
      "type": "NUMBER",
      "metadata": {
        "value": "7"
      },
      "mentions": [
        {
          "text": {
            "content": "7",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW, Washington, DC",
      "type": "ADDRESS",
      "metadata": {
        "locality": "Washington",
        "narrow_region": "District of Columbia",
        "street_name": "Pennsylvania Avenue Northwest",
        "street_number": "1600",
        "broad_region": "District of Columbia",
        "country": "US"
      },
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW, Washington, DC",
            "beginOffset": -1
          },
          "type": "TYPE_UNKNOWN",
          "probability": 1
        }
      ]
    },
    {
      "name": "1600 Pennsylvania Ave NW",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.901
        }
      ]
    },
    {
      "name": "President",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "President",
            "beginOffset": -1
          },
          "type": "COMMON",
          "probability": 0.941
        }
      ]
    },
    {
      "name": "Trump",
      "type": "PERSON",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Trump",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.948
        }
      ]
    },
    {
      "name": "Washington, DC",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "Washington, DC",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.92
        }
      ]
    },
    {
      "name": "White House",
      "type": "LOCATION",
      "metadata": {},
      "mentions": [
        {
          "text": {
            "content": "White House",
            "beginOffset": -1
          },
          "type": "PROPER",
          "probability": 0.785
        }
      ]
    }
  ],
  "languageCode": "en",
  "languageSupported": true
}

A matriz entities contém Entity objetos que representam as entidades detetadas, que incluem informações como o nome e o tipo da entidade.

Go


func analyzeEntitiesFromGCS(ctx context.Context, gcsURI string) (*languagepb.AnalyzeEntitiesResponse, error) {
	return client.AnalyzeEntities(ctx, &languagepb.AnalyzeEntitiesRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_GcsContentUri{
				GcsContentUri: gcsURI,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
		EncodingType: languagepb.EncodingType_UTF8,
	})
}

Java

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  // Set the GCS Content URI path to the file to be analyzed
  Document doc =
      Document.newBuilder().setGcsContentUri(gcsUri).setType(Type.PLAIN_TEXT).build();
  AnalyzeEntitiesRequest request =
      AnalyzeEntitiesRequest.newBuilder()
          .setDocument(doc)
          .setEncodingType(EncodingType.UTF16)
          .build();

  AnalyzeEntitiesResponse response = language.analyzeEntities(request);

  // Print the response
  for (Entity entity : response.getEntitiesList()) {
    System.out.printf("Entity: %s\n", entity.getName());
    System.out.println("Metadata: ");
    for (Map.Entry<String, String> entry : entity.getMetadataMap().entrySet()) {
      System.out.printf("%s : %s", entry.getKey(), entry.getValue());
    }
    for (EntityMention mention : entity.getMentionsList()) {
      System.out.printf("Begin offset: %d\n", mention.getText().getBeginOffset());
      System.out.printf("Content: %s\n", mention.getText().getContent());
      System.out.printf("Type: %s\n\n", mention.getType());
      System.out.printf("Probability: %s\n\n", mention.getProbability());
    }
  }
}

Node.js

// Imports the Google Cloud client library
const language = require('@google-cloud/language').v2;

// Creates a client
const client = new language.LanguageServiceClient();

/**
 * TODO(developer): Uncomment the following lines to run this code
 */
// const bucketName = 'Your bucket name, e.g. my-bucket';
// const fileName = 'Your file name, e.g. my-file.txt';

// Prepares a document, representing a text file in Cloud Storage
const document = {
  gcsContentUri: `gs://${bucketName}/${fileName}`,
  type: 'PLAIN_TEXT',
};

// Detects entities in the document
const [result] = await client.analyzeEntities({document});
const entities = result.entities;

console.log('Entities:');
entities.forEach(entity => {
  console.log(entity.name);
  console.log(` - Type: ${entity.type}`);
  if (entity.metadata) {
    console.log(` - Metadata: ${entity.metadata}`);
  }
});

Python

from google.cloud import language_v2


def sample_analyze_entities(
    gcs_content_uri: str = "gs://cloud-samples-data/language/entity.txt",
) -> None:
    """
    Analyzes Entities in text file stored in Cloud Storage.

    Args:
      gcs_content_uri: Google Cloud Storage URI where the file content is located.
        e.g. gs://[Your Bucket]/[Path to File]
    """

    client = language_v2.LanguageServiceClient()

    # Available types: PLAIN_TEXT, HTML
    document_type_in_plain_text = language_v2.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    # https://cloud.google.com/natural-language/docs/languages
    language_code = "en"
    document = {
        "gcs_content_uri": gcs_content_uri,
        "type_": document_type_in_plain_text,
        "language_code": language_code,
    }

    # Available values: NONE, UTF8, UTF16, UTF32.
    # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType.
    encoding_type = language_v2.EncodingType.UTF8

    response = client.analyze_entities(
        request={"document": document, "encoding_type": encoding_type}
    )

    for entity in response.entities:
        print(f"Representative name for the entity: {entity.name}")

        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al.
        # See https://cloud.google.com/natural-language/docs/reference/rest/v2/Entity#type.
        print(f"Entity type: {language_v2.Entity.Type(entity.type_).name}")

        # Loop over the metadata associated with entity.
        # Some entity types may have additional metadata, e.g. ADDRESS entities
        # may have metadata for the address street_name, postal_code, et al.
        for metadata_name, metadata_value in entity.metadata.items():
            print(f"{metadata_name}: {metadata_value}")

        # Loop over the mentions of this entity in the input document.
        # The API currently supports proper noun mentions.
        for mention in entity.mentions:
            print(f"Mention text: {mention.text.content}")

            # Get the mention type, e.g. PROPER for proper noun
            print(
                "Mention type:" f" {language_v2.EntityMention.Type(mention.type_).name}"
            )

            # Get the probability score associated with the first mention of the entity in the (0, 1.0] range.
            print(f"Probability score: {mention.probability}")

    # Get the language of the text, which will be the same as
    # the language specified in the request or, if not specified,
    # the automatically-detected language.
    print(f"Language of the text: {response.language_code}")

Idiomas adicionais

C#: Siga as instruções de configuração do C# na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para .NET.

PHP: Siga as instruções de configuração do PHP na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para PHP.

Ruby: Siga as instruções de configuração do Ruby na página de bibliotecas cliente e, em seguida, visite a documentação de referência da linguagem natural para Ruby.

A analisar entidades Mantenha tudo organizado com as coleções Salve e categorize o conteúdo com base nas suas preferências.

Analisar entidades numa string

Protocolo

gcloud

Go

Java

Node.js

Python

Idiomas adicionais

Analisar entidades a partir do Cloud Storage

Protocolo

gcloud

Go

Java

Node.js

Python

Idiomas adicionais

A analisar entidades