KnowledgeAnswersIntentQueryToken

AI Overview😉

  • The potential purpose of this module is to analyze search queries and identify the intent behind the query, including the specific keywords and phrases that are relevant to the user's question. This involves tokenizing the query, assigning prior scores to each token based on their importance, and identifying synonyms and related phrases.
  • This module could impact search results by allowing Google to better understand the context and intent behind a user's query, and to return more relevant and accurate results. For example, if a user searches for "Height of Barack Obama", the module would identify "Height" as the key intent token, and return results that are relevant to that specific question. This could lead to more accurate and relevant search results, and a better user experience.
  • To be more favorable to this function, a website could focus on creating high-quality, relevant, and informative content that answers specific user questions and addresses their intent. This could involve using natural language and long-tail keywords in content, as well as optimizing for specific user queries and intents. Additionally, websites could focus on creating clear and concise metadata, such as title tags and descriptions, that accurately reflect the content of the page and help Google understand its relevance to specific user queries.

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GoogleApi.ContentWarehouse.V1.Model.KnowledgeAnswersIntentQueryToken (google_api_content_warehouse v0.4.0)

A token represents an ngram with relevant information about it. If the token is a context phrase, it will have a prior score associated with it. The prior is computed via knowledge/answers/query_generalization/ word_prior/word_prior_from_examples_lib.cc, and ranges between 0 and 1. Stopwords and intent tokens (primary and component) have a score of 1.0.

Attributes

  • evalData (type: GoogleApi.ContentWarehouse.V1.Model.NlpSemanticParsingAnnotationEvalData.t, default: nil) - This field is used inside Aqua and outside Aqua for identifying the token indices and/or byte offsets of this Token.
  • ngram (type: String.t, default: nil) - |ngram| should be populated with a string from the raw query, not the normalized tokens. E.g. The ngram in the ignored token for the Height intent on the query [Height of barack obama], will be "Height". The ngram in the ignored token for the Videos intent on the query [vidéos] will be "vidéos".
  • parsedDueToExperiment (type: list(String.t), default: nil) - Experiments that caused this Token to parse, without which this would not have parsed.
  • prior (type: number(), default: nil) -
  • provenance (type: String.t, default: nil) -
  • provenanceId (type: list(String.t), default: nil) - Unique identifiers for the provenance of this token, for example, NLP Repository Example IDs.
  • provenanceLanguage (type: String.t, default: nil) -
  • synonyms (type: list(GoogleApi.ContentWarehouse.V1.Model.KnowledgeAnswersIntentQueryTokenSynonym.t), default: nil) -

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

Link to this type

t()

@type t() :: %GoogleApi.ContentWarehouse.V1.Model.KnowledgeAnswersIntentQueryToken{
  evalData:
    GoogleApi.ContentWarehouse.V1.Model.NlpSemanticParsingAnnotationEvalData.t()
    | nil,
  ngram: String.t() | nil,
  parsedDueToExperiment: [String.t()] | nil,
  prior: number() | nil,
  provenance: String.t() | nil,
  provenanceId: [String.t()] | nil,
  provenanceLanguage: String.t() | nil,
  synonyms:
    [
      GoogleApi.ContentWarehouse.V1.Model.KnowledgeAnswersIntentQueryTokenSynonym.t()
    ]
    | nil
}

Functions

Link to this function

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.