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Menganalisis entity di file Cloud Storage

Periksa teks dalam file yang disimpan di Cloud Storage untuk menemukan entity umum (nama diri seperti tokoh publik, dan landmark), dan tampilkan informasi tentang entity tersebut.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

Menganalisis Entity

Contoh kode

Go

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Natural Language, lihat Library klien Natural Language. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi Go API Natural Language.

Untuk melakukan autentikasi ke Natural Language, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.


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

Untuk melakukan autentikasi ke Natural Language, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

// 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

Untuk melakukan autentikasi ke Natural Language, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

// 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}`);
  }
});

PHP

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Natural Language, lihat Library klien Natural Language.

Untuk melakukan autentikasi ke Natural Language, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

use Google\Cloud\Language\V1\AnalyzeEntitiesRequest;
use Google\Cloud\Language\V1\Client\LanguageServiceClient;
use Google\Cloud\Language\V1\Document;
use Google\Cloud\Language\V1\Document\Type;
use Google\Cloud\Language\V1\Entity\Type as EntityType;

/**
 * @param string $uri The cloud storage object to analyze (gs://your-bucket-name/your-object-name)
 */
function analyze_entities_from_file(string $uri): void
{
    // Create the Natural Language client
    $languageServiceClient = new LanguageServiceClient();

    // Create a new Document, pass GCS URI and set type to PLAIN_TEXT
    $document = (new Document())
        ->setGcsContentUri($uri)
        ->setType(Type::PLAIN_TEXT);

    // Call the analyzeEntities function
    $request = (new AnalyzeEntitiesRequest())
        ->setDocument($document);
    $response = $languageServiceClient->analyzeEntities($request);
    $entities = $response->getEntities();
    // Print out information about each entity
    foreach ($entities as $entity) {
        printf('Name: %s' . PHP_EOL, $entity->getName());
        printf('Type: %s' . PHP_EOL, EntityType::name($entity->getType()));
        printf('Salience: %s' . PHP_EOL, $entity->getSalience());
        if ($entity->getMetadata()->offsetExists('wikipedia_url')) {
            printf('Wikipedia URL: %s' . PHP_EOL, $entity->getMetadata()->offsetGet('wikipedia_url'));
        }
        if ($entity->getMetadata()->offsetExists('mid')) {
            printf('Knowledge Graph MID: %s' . PHP_EOL, $entity->getMetadata()->offsetGet('mid'));
        }
        printf(PHP_EOL);
    }
}

Python

Untuk melakukan autentikasi ke Natural Language, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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}")

Langkah selanjutnya

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