Google llc (20240202519). GENERATING VECTOR REPRESENTATIONS OF DOCUMENTS simplified abstract

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GENERATING VECTOR REPRESENTATIONS OF DOCUMENTS

Organization Name

google llc

Inventor(s)

Quoc V. Le of Sunnyvale CA (US)

GENERATING VECTOR REPRESENTATIONS OF DOCUMENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240202519 titled 'GENERATING VECTOR REPRESENTATIONS OF DOCUMENTS

Simplified Explanation: The patent application describes methods, systems, and apparatus for generating document vector representations using a trained neural network system.

Key Features and Innovation:

  • Obtaining a new document and determining a vector representation using a trained neural network system.
  • The neural network system predicts the likelihood of words following each other in a sequence within the input document.
  • Iteratively providing sequences of words to the neural network system to determine the vector representation using gradient descent.

Potential Applications: This technology can be applied in natural language processing, text analysis, document classification, and information retrieval systems.

Problems Solved: This technology addresses the need for efficient and accurate document vector representations for various text-based applications.

Benefits:

  • Improved accuracy in document vector representations.
  • Enhanced performance in natural language processing tasks.
  • Increased efficiency in text analysis and information retrieval systems.

Commercial Applications: The technology can be utilized in search engines, recommendation systems, sentiment analysis tools, and automated content generation platforms.

Prior Art: Researchers can explore prior work in neural network-based document vector representations and natural language processing systems.

Frequently Updated Research: Stay updated on advancements in neural network training techniques, document representation methods, and text analysis algorithms.

Questions about Document Vector Representations: 1. How does the trained neural network system predict the likelihood of words following each other in a sequence? 2. What are the potential limitations of using gradient descent for determining document vector representations?


Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating document vector representations. one of the methods includes obtaining a new document; and determining a vector representation for the new document using a trained neural network system, wherein the trained neural network system has been trained to receive an input document and a sequence of words from the input document and to generate a respective word score for each word in a set of words, wherein each of the respective word scores represents a predicted likelihood that the corresponding word follows a last word in the sequence in the input document, and wherein determining the vector representation for the new document using the trained neural network system comprises iteratively providing each of the plurality of sequences of words to the trained neural network system to determine the vector representation for the new document using gradient descent.

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