Patents by Inventor Wenpeng LU

Wenpeng LU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method and device for chinese concept embedding generation based on wikipedia link structure
    Patent number: 11244020
    Abstract: A method and a device for Chinese concept embedding generation based on Wikipedia link structure includes: Step (1): According to the title concepts and/or link concepts in Chinese Wikipedia pages, a link information database is constructed; Step (2): For the title concepts, according to their link relationships with link concepts in the link information database, the positive and negative training instances are constructed respectively, which constitute the training dataset; Step (3): A concept embedding model is built, including an input layer, an embedding layer, a computational operation layer, and an output layer; Step (4): The concept embedding model is trained with the training dataset, then, the Chinese concept embedding is extracted/generated from the concept embedding model. The method can accurately distinguish different concepts and overcome the problem of polysemy that troubles the traditional embedding methods, which is beneficial to generate more accurate concept embedding representation.
    Type: Grant
    Filed: October 26, 2018
    Date of Patent: February 8, 2022
    Assignee: QILU UNIVERSITY OF TECHNOLOGY
    Inventor: Wenpeng Lu
  • METHOD AND DEVICE FOR CHINESE CONCEPT EMBEDDING GENERATION BASED ON WIKIPEDIA LINK STRUCTURE
    Publication number: 20210073307
    Abstract: A method and a device for Chinese concept embedding generation based on Wikipedia link structure includes: Step (1): According to the title concepts and/or link concepts in Chinese Wikipedia pages, a link information database is constructed; Step (2): For the title concepts, according to their link relationships with link concepts in the link information database, the positive and negative training instances are constructed respectively, which constitute the training dataset; Step (3): A concept embedding model is built, including an input layer, an embedding layer, a computational operation layer, and an output layer; Step (4): The concept embedding model is trained with the training dataset, then, the Chinese concept embedding is extracted/generated from the concept embedding model. The method can accurately distinguish different concepts and overcome the problem of polysemy that troubles the traditional embedding methods, which is beneficial to generate more accurate concept embedding representation.
    Type: Application
    Filed: October 26, 2018
    Publication date: March 11, 2021
    Applicant: QILU UNIVERSITY OF TECHNOLOGY
    Inventor: Wenpeng LU