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Analiza entidades en un archivo de texto

Inspecciona texto en busca de entidades conocidas (sustantivos propios, como figuras públicas y puntos de referencia), y muestra información sobre ellas.

Páginas de documentación que incluyen esta muestra de código

Para ver la muestra de código usada en contexto, consulta la siguiente documentación:

Muestra de código


        private static void AnalyzeEntitiesFromText(string text)
            var client = LanguageServiceClient.Create();
            var response = client.AnalyzeEntities(new Document()
                Content = text,
                Type = Document.Types.Type.PlainText

        private static void WriteEntities(IEnumerable<Entity> entities)
            foreach (var entity in entities)
                Console.WriteLine($"\tName: {entity.Name}");
                Console.WriteLine($"\tType: {entity.Type}");
                Console.WriteLine($"\tSalience: {entity.Salience}");
                foreach (var mention in entity.Mentions)
                    Console.WriteLine($"\t\t{mention.Text.BeginOffset}: {mention.Text.Content}");
                foreach (var keyval in entity.Metadata)
                    Console.WriteLine($"\t\t{keyval.Key}: {keyval.Value}");


func analyzeEntities(ctx context.Context, client *language.Client, text string) (*languagepb.AnalyzeEntitiesResponse, error) {
	return client.AnalyzeEntities(ctx, &languagepb.AnalyzeEntitiesRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			Type: languagepb.Document_PLAIN_TEXT,
		EncodingType: languagepb.EncodingType_UTF8,


// Instantiate the Language client
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();
  AnalyzeEntitiesRequest request =

  AnalyzeEntitiesResponse response = language.analyzeEntities(request);

  // Print the response
  for (Entity entity : response.getEntitiesList()) {
    System.out.printf("Entity: %s", entity.getName());
    System.out.printf("Salience: %.3f\n", entity.getSalience());
    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());


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

// 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;

entities.forEach(entity => {
  console.log(` - Type: ${entity.type}, Salience: ${entity.salience}`);
  if (entity.metadata && entity.metadata.wikipedia_url) {
    console.log(` - Wikipedia URL: ${entity.metadata.wikipedia_url}`);


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

/** Uncomment and populate these variables in your code */
// $text = 'The text to analyze.';

// Create the Natural Language client
$languageServiceClient = new LanguageServiceClient();
try {
    // Create a new Document, add text as content and set type to PLAIN_TEXT
    $document = (new Document())

    // Call the analyzeEntities function
    $response = $languageServiceClient->analyzeEntities($document, []);
    $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'));
} finally {


from import language_v1

def sample_analyze_entities(text_content):
    Analyzing Entities in a String

      text_content The text content to analyze

    client = language_v1.LanguageServiceClient()

    # text_content = 'California is a state.'

    # Available types: PLAIN_TEXT, HTML
    type_ = language_v1.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    language = "en"
    document = {"content": text_content, "type_": type_, "language": language}

    # Available values: NONE, UTF8, UTF16, UTF32
    encoding_type = language_v1.EncodingType.UTF8

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

    # Loop through entitites returned from the API
    for entity in response.entities:
        print(u"Representative name for the entity: {}".format(

        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
        print(u"Entity type: {}".format(language_v1.Entity.Type(entity.type_).name))

        # Get the salience score associated with the entity in the [0, 1.0] range
        print(u"Salience score: {}".format(entity.salience))

        # Loop over the metadata associated with entity. For many known entities,
        # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).
        # 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(u"{}: {}".format(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(u"Mention text: {}".format(mention.text.content))

            # Get the mention type, e.g. PROPER for proper noun
                u"Mention type: {}".format(language_v1.EntityMention.Type(mention.type_).name)

    # 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(u"Language of the text: {}".format(response.language))


# text_content = "Text to extract entities from"

require "google/cloud/language"

language = Google::Cloud::Language.language_service

document = { content: text_content, type: :PLAIN_TEXT }
response = language.analyze_entities document: document

entities = response.entities

entities.each do |entity|
  puts "Entity #{} #{entity.type}"

  puts "URL: #{entity.metadata['wikipedia_url']}" if entity.metadata["wikipedia_url"]