NlpSemanticParsingAnnotationEvalData

AI Overview😉

  • The potential purpose of this module is to evaluate the accuracy of natural language processing (NLP) models in identifying specific spans of text within a query or utterance. This module helps to assess how well the model can identify the correct tokens or bytes within a sentence that correspond to a particular annotation or entity.
  • This module could impact search results by influencing the ranking of pages based on their relevance to the user's query. If a page's content is accurately annotated with specific entities or concepts, it may be considered more relevant to the user's search query, leading to a higher ranking. Conversely, pages with inaccurate or incomplete annotations may be ranked lower.
  • To be more favorable for this function, a website could focus on providing high-quality, accurately annotated content that clearly identifies specific entities, concepts, and relationships within the text. This could involve using schema.org markup, entity disambiguation, and other semantic annotation techniques to provide a clear understanding of the content's meaning and relevance. Additionally, ensuring that the website's content is well-structured, with clear headings, paragraphs, and other organizational elements, could also help the NLP model to more accurately identify relevant spans of text.

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

Annotators whose semantics are represented via a protocol message should add to that message a field or extension of this type and set it using Annotator::PopulateAnnotationEvalData to enable span-based evaluation metrics in training. Evaluation is done based on token spans. The byte span aligns with the token span and is used when saving examples. Background: In some settings, the examples used to induce/train a grammar do not specify complete semantics of an annotation. For example, some examples that come from Ewok specify only the span associated with each annotation. This message allows evaluation metrics to test the span by embedding it in the semantics. LINT.IfChange

Attributes

  • additionalSpans (type: list(GoogleApi.ContentWarehouse.V1.Model.NlpSemanticParsingAnnotationEvalData.t), default: nil) - Additional spans after the first. Empty in all additional_spans.
  • numBytes (type: integer(), default: nil) -
  • numTokens (type: integer(), default: nil) -
  • startByte (type: integer(), default: nil) - Byte position within the utterance. Safe to use across different components of the NLU stack as long as said components have access to the same query.
  • startToken (type: integer(), default: nil) - Token position. This is cleared when normalizing examples for storage because tokenization changes over time. DO NOT use these two fields across components that use different tokenizations.

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.NlpSemanticParsingAnnotationEvalData{
    additionalSpans: [t()] | nil,
    numBytes: integer() | nil,
    numTokens: integer() | nil,
    startByte: integer() | nil,
    startToken: integer() | nil
  }

Functions

Link to this function

decode(value, options)

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

Unwrap a decoded JSON object into its complex fields.