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Patent Agency Ranking
公开(公告)号：US11366973B2
公开(公告)日：2022-06-21
申请号：US16691104
申请日：2019-11-21
Inventor： Jingwei Wang , Ao Zhang , Jiaxiang Liu , Yu Sun , Zhi Li
IPC: G06F40/35 , G06F40/186 , G06F40/289
Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a topic. A specific embodiment of the method comprises: determining a to-be-recognized sentence sequence; calculating similarities between the to-be-recognized sentence sequence and each of topic templates in a topic template set in a target area, the each of the topic templates in the topic template set corresponding to a topic in at least one topic in the target area, the topic template including a topic section sequence, and a topic section including a topic sentence sequence; and determining a topic of the to-be-recognized sentence sequence according to an associated parameter, the associated parameter including the similarities between the to-be-recognized sentence sequence and the each of the topic templates in the topic template set. This embodiment reduces labor costs during a topic segmentation.
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2.发明授权公开(公告)号：US11914964B2
公开(公告)日：2024-02-27
申请号：US17209124
申请日：2021-03-22
Inventor： Shuohuan Wang , Jiaxiang Liu , Xuan Ouyang , Yu Sun , Hua Wu , Haifeng Wang
Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes: acquiring a semantic representation model which has been trained for a first language as a first semantic representation model; taking a bottom layer and a top layer of the first semantic representation model as trained layers, initializing the trained layers, keeping model parameters of other layers unchanged, and training the trained layers using training language materials of a second language until a training ending condition is met; successively bringing the untrained layers into the trained layers from bottom to top, and executing these layers respectively: keeping the model parameters of other layers than the trained layers unchanged, and training the trained layers using the training language materials of the second language until the training ending condition is met respectively; and obtaining a semantic representation model for the second language after all the layers are trained.
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3.发明申请公开(公告)号：US20220004716A1
公开(公告)日：2022-01-06
申请号：US17209124
申请日：2021-03-22
Inventor： Shuohuan Wang , Jiaxiang Liu , Xuan Ouyang , Yu Sun , Hua Wu , Haifeng Wang
Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence. An implementation includes: acquiring a semantic representation model which has been trained for a first language as a first semantic representation model; taking a bottom layer and a top layer of the first semantic representation model as trained layers, initializing the trained layers, keeping model parameters of other layers unchanged, and training the trained layers using training language materials of a second language until a training ending condition is met; successively bringing the untrained layers into the trained layers from bottom to top, and executing these layers respectively: keeping the model parameters of other layers than the trained layers unchanged, and training the trained layers using the training language materials of the second language until the training ending condition is met respectively; and obtaining a semantic representation model for the second language after all the layers are trained.
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公开(公告)号：US20200210522A1
公开(公告)日：2020-07-02
申请号：US16691104
申请日：2019-11-21
Inventor： Jingwei Wang , Ao Zhang , Jiaxiang Liu , Yu Sun , Zhi Li
Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a topic. A specific embodiment of the method comprises: determining a to-be-recognized sentence sequence; calculating similarities between the to-be-recognized sentence sequence and each of topic templates in a topic template set in a target area, the each of the topic templates in the topic template set corresponding to a topic in at least one topic in the target area, the topic template including a topic section sequence, and a topic section including a topic sentence sequence; and determining a topic of the to-be-recognized sentence sequence according to an associated parameter, the associated parameter including the similarities between the to-be-recognized sentence sequence and the each of the topic templates in the topic template set. This embodiment reduces labor costs during a topic segmentation.
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公开(公告)号：US20210342549A1
公开(公告)日：2021-11-04
申请号：US17375156
申请日：2021-07-14
Inventor： Jiaxiang Liu , Shikun Feng
IPC: G06F40/30 , G06F40/58 , G06N20/00 , G06F16/901
Abstract: The disclosure provides a method for training a semantic analysis model, an electronic device and a storage medium. The method includes: obtaining a plurality of training data, in which each of the plurality of training data comprises a search word, information on at least one text obtained by searching the search word, and at least one associated word corresponding to the at least one text; constructing a graph model based on the training data, and determining target training data from the plurality of training data by using the graph model, the target training data comprising search word samples, information samples and associated word samples; and training a semantic analysis model based on the search word samples, the information samples, and the associated word samples.
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