公开(公告)号：US12190061B2

公开(公告)日：2025-01-07

申请号：US17644856

申请日：2021-12-17

Applicant: ADOBE INC.

Inventor： Shashank Shailabh ,  Madhur Panwar ,  Milan Aggarwal ,  Pinkesh Badjatiya ,  Simra Shahid ,  Nikaash Puri ,  S Sejal Naidu ,  Sharat Chandra Racha ,  Balaji Krishnamurthy ,  Ganesh Karbhari Palwe

IPC: G06F40/289 ,  G06F40/30 ,  G06F40/40

Abstract: Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.

公开(公告)号：US20230169271A1

公开(公告)日：2023-06-01

申请号：US17644856

申请日：2021-12-17

Applicant: ADOBE INC.

Inventor： Shashank Shailabh ,  Madhur Panwar ,  Milan Aggarwal ,  Pinkesh Badjatiya ,  Simra Shahid ,  Nikaash Puri ,  S Sejal Naidu ,  Sharat Chandra Racha ,  Balaji Krishnamurthy ,  Ganesh Karbhari Palwe

IPC: G06F40/289 ,  G06F40/40 ,  G06F40/30

CPC classification number: G06F40/289 ,  G06F40/40 ,  G06F40/30

Abstract: Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.