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Analiza entidades

El análisis de entidades inspecciona el texto dado en busca de entidades conocidas (nombres propios como figuras públicas, puntos de referencia, etc.) y muestra información sobre ellas. Se ejecuta con el método analyzeEntities. Para obtener información sobre los tipos de entidades que identifica Natural Language, consulta la documentación de Entidad. Para obtener información sobre los idiomas compatibles con la API de Natural Language, consulta Idiomas compatibles.

Esta sección muestra algunas maneras de detectar entidades en un documento. Para cada documento, debes enviar una solicitud diferente.

Analiza entidades en una string

Este es un ejemplo de la realización de un análisis de entidades en una string de texto enviado directamente a la API de Natural Language:

Protocolo

Para analizar entidades en un documento, realiza una solicitud POST al método documents:analyzeEntities de REST y proporciona el cuerpo de solicitud adecuado, como se muestra en el siguiente ejemplo.

En este ejemplo, se usa el comando gcloud auth application-default print-access-token a fin de obtener un token de acceso correspondiente a una cuenta de servicio configurada para el proyecto con la CLI de gcloud de Google Cloud Platform. Para obtener instrucciones sobre cómo instalar la CLI de gcloud y configurar un proyecto con una cuenta de servicio, consulta la Guía de inicio 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"

Si no especificas document.language_code, el idioma se detectará automáticamente. Para obtener información sobre los idiomas compatibles con la API de Natural Language, consulta Idiomas compatibles. Consulta la documentación de referencia de Document para obtener más información sobre la configuración del cuerpo de la solicitud.

Si la solicitud se completa correctamente, el servidor muestra un código de estado HTTP 200 OK y la respuesta en 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
}

El arreglo entities contiene objetos Entity que representan las entidades detectadas, incluida información sobre el nombre y el tipo de entidad.

gcloud

Consulta el comando analyze-entities para obtener todos los detalles.

Para realizar un análisis de entidades, usa la CLI de gcloud y también utiliza la marca --content para identificar el contenido que se analizará:

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."

Si la solicitud es exitosa, el servidor muestra una respuesta en 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"
}

El arreglo entities contiene objetos Entity que representan las entidades detectadas, incluida información sobre el nombre y el tipo de entidad.

Go

Si deseas obtener información sobre cómo instalar y usar la biblioteca cliente de Natural Language, consulta Bibliotecas cliente de Natural Language. Para obtener más información, consulta la documentación de referencia de la API de Natural Language Go.

Para autenticarte en Natural Language, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo 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

Para obtener información sobre cómo instalar y usar la biblioteca cliente de Natural Language, consulta Bibliotecas cliente de Natural Language. Para obtener más información, consulta la documentación de referencia de la API de Natural Language 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 adicionales

C#: Sigue las instrucciones de configuración de C# en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para .NET.

PHP: Sigue las instrucciones de configuración de PHP en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para PHP.

Ruby: Sigue las instrucciones de configuración de Ruby en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para Ruby.

Analiza entidades en Cloud Storage

Para tu comodidad, la API de Natural Language puede realizar un análisis de entidades directamente en un archivo ubicado en Cloud Storage, sin la necesidad de enviar el contenido del archivo en el cuerpo de tu solicitud.

El siguiente es un ejemplo de cómo realizar un análisis de entidades en un archivo ubicado en Cloud Storage.

Protocolo

Para analizar entidades en un documento almacenado en Cloud Storage, realiza una solicitud POST al método documents:analyzeEntities de REST y proporciona el cuerpo de la solicitud adecuado con la ruta del documento, como se muestra en el siguiente ejemplo.

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"

Si la solicitud se completa correctamente, el servidor muestra un código de estado HTTP 200 OK y la respuesta en 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
}

El arreglo entities contiene objetos Entity que representan las entidades detectadas, incluida información sobre el nombre y el tipo de entidad.

gcloud

Consulta el comando analyze-entities para obtener todos los detalles.

Para realizar un análisis de entidades en un archivo de Cloud Storage, usa la herramienta de línea de comandos gcloud y también utiliza la marca --content-file para identificar la ruta del archivo que tiene el contenido que analizarás:

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

Si la solicitud es exitosa, el servidor muestra una respuesta en 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
}

El arreglo entities contiene objetos Entity que representan las entidades detectadas, incluida información sobre el nombre y el tipo de entidad.

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 adicionales

C#: Sigue las instrucciones de configuración de C# en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para .NET.

PHP: Sigue las instrucciones de configuración de PHP en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para PHP.

Ruby: Sigue las instrucciones de configuración de Ruby en la página Bibliotecas cliente y, luego, visita la documentación de referencia de Natural Language para Ruby.