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    Systems and Methods for Machine-Learned Prediction of Semantic Similarity Between Documents
    2.
    发明申请
    Systems and Methods for Machine-Learned Prediction of Semantic Similarity Between Documents 有权

    公开(公告)号：US20220129638A1

    公开(公告)日：2022-04-28

    申请号：US17078569

    申请日：2020-10-23

    Applicant: Google LLC

    Inventor： Liu Yang ,  Marc Najork ,  Michael Bendersky ,  Mingyang Zhang ,  Cheng Li

    IPC: G06F40/30 ,  G06F40/205 ,  G06N3/04 ,  G06N3/08

    Abstract: Systems and methods of the present disclosure are directed to a method for predicting semantic similarity between documents. The method can include obtaining a first document and a second document. The method can include parsing the first document into a plurality of first textual blocks and the second document into a plurality of second textual blocks. The method can include processing each of the plurality of first textual blocks and the second textual blocks with a machine-learned semantic document encoding model to obtain a first document encoding and a second document encoding. The method can include determining a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding.

    System for Information Extraction from Form-Like Documents
    3.
    发明申请
    System for Information Extraction from Form-Like Documents 有权

    公开(公告)号：US20210374395A1

    公开(公告)日：2021-12-02

    申请号：US16890287

    申请日：2020-06-02

    Applicant: Google LLC

    Inventor： Sandeep Tata ,  Bodhisattwa Prasad Majumder ,  Qi Zhao ,  James Bradley Wendt ,  Marc Najork ,  Navneet Potti

    IPC: G06K9/00 ,  G06K9/20 ,  G06K9/62 ,  G06T7/70 ,  G06K9/72 ,  G06N20/00 ,  G06N5/04

    Abstract: The present disclosure is directed to extracting text from form-like documents. In particular, a computing system can obtain an image of a document that contains a plurality of portions of text. The computing system can extract one or more candidate text portions for each field type included in a target schema. The computing system can generate a respective input feature vector for each candidate for the field type. The computing system can generate a respective candidate embedding for the candidate text portion. The computing system can determine a respective score for each candidate text portion for the field type based at least in part on the respective candidate embedding for the candidate text portion. The computing system can assign one or more of the candidate text portions to the field type based on the respective scores.

    Systems and Methods for Active Learning
    4.
    发明申请
    Systems and Methods for Active Learning 审中-公开

    公开(公告)号：US20200250527A1

    公开(公告)日：2020-08-06

    申请号：US16750053

    申请日：2020-01-23

    Applicant: Google LLC

    Inventor： Qi Zhao ,  Abbas Kazerouni ,  Sandeep Tata ,  Jing Xie ,  Marc Najork

    IPC: G06N3/08 ,  G06N3/04

    Abstract: The present disclosure provides computing systems and methods directed to active learning and may provide advantages or improvements to active learning applications for skewed data sets. A challenge in training and developing high-quality models for many supervised learning scenarios is obtaining labeled training examples. This disclosure provides systems and methods for active learning on a training dataset that includes both labeled and unlabeled datapoints. In particular, the systems and methods described herein can select (e.g., at each of a number of iterations) a number of the unlabeled datapoints for which labels should be obtained to gain additional labeled datapoints on which to train a machine-learned model (e.g., machine-learned classifier model). Generally, the disclosure provides cost-effective methods and systems for selecting data to improve machine-learned models in applications such as the identification of content items in text, images, and/or audio.

    Systems and methods for machine-learned prediction of semantic similarity between documents
    5.
    发明授权
    Systems and methods for machine-learned prediction of semantic similarity between documents 有权

    公开(公告)号：US12210837B2

    公开(公告)日：2025-01-28

    申请号：US18321424

    申请日：2023-05-22

    Applicant: Google LLC

    Inventor： Liu Yang ,  Marc Najork ,  Michael Bendersky ,  Mingyang Zhang ,  Cheng Li

    IPC: G06F40/30 ,  G06F40/205 ,  G06N3/045 ,  G06N3/08

    Abstract: Systems and methods of the present disclosure are directed to a method for predicting semantic similarity between documents. The method can include obtaining a first document and a second document. The method can include parsing the first document into a plurality of first textual blocks and the second document into a plurality of second textual blocks. The method can include processing each of the plurality of first textual blocks and the second textual blocks with a machine-learned semantic document encoding model to obtain a first document encoding and a second document encoding. The method can include determining a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding.

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