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公开(公告)号：US20190058609A1
公开(公告)日：2019-02-21
申请号：US16054812
申请日：2018-08-03
Inventor： Jianqing CUI , Yingchao SHI , Hao TIAN , Qiaoqiao SHE , Shiqi ZHAO
Abstract: Embodiments of the disclosure disclose a method and apparatus for pushing information based on artificial intelligence. A specific embodiment of the method includes: mining, in response to a new match occurring, real-time match data of the match and real-time associated data of the match; generating structured data using the real-time match data; generating a to-be-recommended item using the real-time associated data and an offline item; determining whether a current time point is a recommendation node based on the structured data and real-time match state information acquired from a state manager; generating a to-be-pushed message based on the to-be-recommended item, the real-time match state information, and basic match information acquired from the state manager, and updating a to-be-pushed message record in the state manager, if the current time point is the recommendation node; and pushing the to-be-pushed message. The embodiment has improved the quality and timeliness of pushing a to-be-pushed message.
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2.发明申请公开(公告)号：US20180357508A1
公开(公告)日：2018-12-13
申请号：US16001119
申请日：2018-06-06
Inventor： Jianqing CUI , Yingchao SHI , Hao TIAN , Shiqi ZHAO , Qiaoqiao SHE
CPC classification number: G06K9/629 , G06F17/30551 , G06F17/3069 , G06K9/6232 , G06N5/04
Abstract: The present disclosure provides a method and apparatus for generating a competition commentary based on artificial intelligence, and a storage medium, wherein the method comprises: obtaining commentator's words commentaries and structured data of historical competitions; generating a commentating model according to obtained information; during live broadcast of a competition, determining a corresponding words commentary according to the commentating model with respect to the structured data obtained each time. The solution of the present disclosure can be applied to reduce time delay and improve the accuracy of the commentaries.
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公开(公告)号：US20210200949A1
公开(公告)日：2021-07-01
申请号：US16935040
申请日：2020-07-21
Inventor： Can GAO , Hao LIU , Bolei HE , Xinyan XIAO , Hao TIAN
IPC: G06F40/242 , G06F40/279
Abstract: The present disclosure provides a pre-training method for a sentiment analysis model and an electronic device, which relates to a field of artificial intelligence technologies. The method includes: based on a given seed sentiment dictionary, performing sentimental knowledge detection on a training corpus in a training corpus set, and determining a detection sentiment word and a detection word pair of the training corpus; according to preset mask processing rules, performing mask process on the training corpus to generate a masked corpus; performing encoding and decoding on the masked corpus by using a preset encoder and decoder to determine the detection sentiment word and the detection word pair of the training corpus; and updating the preset encoder and decoder according to a difference between prediction sentiment word and the detection sentiment word, and a difference between prediction word pair and the detection word pair.
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公开(公告)号：US20210182498A1
公开(公告)日：2021-06-17
申请号：US16885358
申请日：2020-05-28
Inventor： Yu SUN , Haifeng WANG , Shuohuan WANG , Yukun LI , Shikun FENG , Hao TIAN , Hua WU
Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.
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Patent Agency Ranking
公开(公告)号：US20190286996A1
公开(公告)日：2019-09-19
申请号：US16317526
申请日：2017-01-23
Inventor： Hao TIAN , Shiqi ZHAO , Zhou XIN , Quan WEN , Wentao MA , Teng XU , Xinnuo XU , Haisong ZHANG , Xiangyang ZHOU , Rui YAN
IPC: G06N5/02 , G06F16/9032 , G06F17/28
Abstract: The present disclosure provides a human-machine interactive method based on artificial intelligence and a human-machine interactive device based on artificial intelligence. The method includes: receiving a query from a user; processing the query based on a pre-generated model, and obtaining an answer with a human conversation style corresponding to the query, wherein the model is generated based on a human conversation corpus; and feeding back the answer to the user.
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6.发明申请公开(公告)号：US20220171941A1
公开(公告)日：2022-06-02
申请号：US17348104
申请日：2021-06-15
Inventor： Xuan OUYANG , Shuohuan WANG , Chao PANG , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
Abstract: The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge. In the present disclosure, the multi-lingual model can be enabled to achieve semantic interaction between different languages and improve the accuracy of the multi-lingual model in learning the semantic representations of the multi-lingual model.
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公开(公告)号：US20220019744A1
公开(公告)日：2022-01-20
申请号：US17319189
申请日：2021-05-13
Inventor： Fei YU , Jiji TANG , Weichong YIN , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
Abstract: A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.
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公开(公告)号：US20210232775A1
公开(公告)日：2021-07-29
申请号：US17031569
申请日：2020-09-24
Inventor： Han ZHANG , Dongling XIAO , Yukun LI , Yu SUN , Hao TIAN , Hua WU , Haifeng WANG
IPC: G06F40/56
Abstract: The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.
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公开(公告)号：US20170255879A1
公开(公告)日：2017-09-07
申请号：US15392017
申请日：2016-12-28
Inventor： Li CHEN , Qian XU , Hao TIAN , Jingzhou HE , Lei SHI , Fan WANG , Shiwei HUANG , Derong ZHENG
CPC classification number: G06N20/00 , G06F16/90335 , G06F16/904 , G06N7/005
Abstract: A searching method and device based on artificial intelligence is provided in the present disclosure. The searching method includes: obtaining a query; obtaining a first search result corresponding to the query according to a Markov Decision Process MDP model; displaying the first search result; and obtaining a reward for the first search result from a user so as to obtain a second search result according to the MDP model, and displaying the second search result. According to the searching method, the interaction with the user may be more effective, the user's demand is better satisfied, and the user experience is improved.
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