Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Chinese Academy of Sciences

Beijing Xiaomi Mobile Software Co., Ltd.

Boe Technology Group Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Beijing Bytedance Network Technology Co., Ltd.

Peking University

Barrot Technology Limited

Nokia Siemens Networks Oy

Yong Zhu

Zhifan Feng

Chunguang Chai

Yang Zhang

Qi Wang

Yabing Shi

Wenbin Jiang

Yajuan Lyu

Haifeng Wang

Ying Li

Hua Wu

Ye Jiang

Shuangjie Li

Shu Wang

Zhijie Liu

Xunchao Song

Ye Xu

Zhou Fang

Chao Lu

Feng He

Haijin Liang

Hu Yang

Kexin Ren

Xiaohan Zhang

Fang Huang

Shuai Chen

Qian Li

Pingping Huang

Yajuan Lv

Guang Yi Xu

Lingyun Wen

Hongbin Liu

Qiang Li

Wei Zheng

Quan Wang

Jie Fan

Jian Zhou

Yangyuan Wang

Yuanyuan Wang

Pengcheng Yuan

Miao Yu

Jinfeng Zhang

Xinwei Feng

Yao Wang

Min Qiao

Huanyu Zhou

Yilin Zhang

Chuangtian Chen

Siqi Wang

Jiacheng Liu

Rukang Li

Zihao Zhang

Xiaoyang Wang

Xing Wu

Meng Tian

Xin Che

Jianping Chen

Jianhui Huang

Wanhong Wang

Weiyu Wang

Feidi Fan

Bingyang Yu

Changqing Wang

Yingping Chen

Xiaojuan Niu

Shangyi Xu

Changxi Yang

Mengjun Hui

Xuefeng Hao

Dongxiang Ye

Yalou Wang

Jiahuan Huang

Yaxin Li

Zheqi Qu

Weihao Xu

Zhaoji Wang

Wanli Chang

Tongyang Liu

Guizhen Yan

Xuesong Liu

Zi Liang Huang

Shoubing Sun

Tang Zhou

A question answering method, a method of training a question answering model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing technology, deep learning technology, and knowledge mapping technology. The question answering method includes: obtaining data to be processed, wherein the data to be processed includes a question and candidate answers; performing general semantic understanding on the data to be processed to obtain a general data feature; selecting a target question answering mode from candidate question answering modes based on the general data feature; and processing the general data feature by using the target question answering mode, to obtain a target answer for the question from the candidate answers.

Question answering method, method of training a question answering model, electronic device, and medium

An information search method includes: obtaining search words at least including a question to be searched and obtaining an initial text vector representation of the search words; obtaining a video corresponding to the search words, and obtaining multi-modality vector representations of the video; starting from the initial text vector representation, performing N rounds of interaction between the video and the search words based on the multi-modality vector representations and a text vector representation of the search words of a current round, to generate a target fusion vector representation, where N is an integer greater than or equal to 1; and obtaining target video frames matching the question to be searched by annotating the video based on the target fusion vector representation.

Information search method and device, electronic device, and storage medium

An application-specific integrated circuit for accelerated encoding and decoding and a method, which are related to the technical field of Bluetooth mobile communication. The application-specific integrated circuit for accelerated encoding and decoding includes: a hardware accelerator, wherein the hardware accelerator includes a pre-processing and pronation processing module, which performs a pre-processing and pronation processing of data, a discrete Fourier transform module is used for performing a multi-level discrete Fourier transform, in an accelerated low-delay modified discrete cosine transform operation LD-MDCT and/or an accelerated the low-delay inverse modified discrete cosine transform operation LD-IMDCT. The application-specific integrated circuit for accelerated encoding and decoding and a method of the present invention adopts an ASIC application-specific integrated circuit, and adopts multi-level discrete Fourier transforms, so that the complex operations are completed by the ASIC application-specific integrated circuit.

Application-specific integrated circuit for accelerating encoding and decoding, and method therefor

The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.

Method of processing triple data, method of training triple data processing model, device, and medium

The present disclosure discloses a method for acquiring a structured question-answering (QA) model, a QA method and corresponding apparatuses, and relates to knowledge graph and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring training samples corresponding to N structured QA database types, the training samples including question samples, information of the structured QA database types and query instruction samples used by the question samples to query structured QA databases of the types, N being an integer greater than 1; and training a text generation model by using the training samples to obtain the structured QA model, wherein the question samples and the information of the structured QA database types are taken as input to the text generation model, and the query instruction samples are taken as target output of the text generation model.

Method for acquiring structured question-answering model, question-answering method and corresponding apparatus

Disclosed are a method for processing multimodal data using a neural network, a device, and a medium, and relates to the field of artificial intelligence and, in particular to multimodal data processing, video classification, and deep learning. The neural network includes: an input subnetwork configured to receive the multimodal data to output respective first features of a plurality of modalities; a plurality of cross-modal feature subnetworks, each of which is configured to receive respective first features of two corresponding modalities to output a cross-modal feature corresponding to the two modalities; a plurality of cross-modal fusion subnetworks, each of which is configured to receive at least one cross-modal feature corresponding to a corresponding target modality and other modalities to output a second feature of the target modality; and an output subnetwork configured to receive respective second features of the plurality of modalities to output a processing result of the multimodal data.

Multimodal data processing

The present disclosure discloses a question analysis method, a device, a knowledge base question answering system and an electronic equipment. The method includes: analyzing a question to obtain N linearized sequences, N being an integer greater than 1; converting the N linearized sequences into N network topology maps; separately calculating a semantic matching degree of each of the N network topology maps to the question; and selecting a network topology map having a highest semantic matching degree to the question as a query graph of the question from the N network topology maps. According to the technology of the present disclosure, the query graph of the question can be obtained more accurately, and the accuracy of the question to the query graph is improved, thereby improving the accuracy of question analysis.

Question analysis method, device, knowledge base question answering system and electronic equipment

A method of processing multimedia data, a device, and a medium, which relates to a field of an artificial intelligence technology, in particular to fields of knowledge graph and deep learning. The method of processing the multimedia data includes: recognizing the multimedia data so as to obtain at least one key information of the multimedia data; querying a predetermined knowledge base according to the at least one key information, so as to determine a multimedia name associated with the at least one key information and an association degree between the multimedia name and the at least one key information; and determining, in the multimedia name, a name of the multimedia data based on a similarity between alternative multimedia data for the multimedia name and the multimedia data, in response to the association degree being less than a first threshold value.

Method of processing multimedia data, device and medium

The present disclosure provides a method for processing intelligent question-answering, an intelligent question-answering system, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, natural language processing technologies, or the like. An implementation includes: acquiring an input question and input data information; and based on the question, the data information and a plurality of knowledge bases, deciding an answer to the question by multilayer appreciation using a plurality of understanding module layers.

Intelligent question-answering processing method and system, electronic device and storage medium

A method for generating a pre-trained language model, includes: obtaining sample files; obtaining typography structure information and text information of the sample files by parsing the sample files; obtaining a plurality of task models of a pre-trained language model; obtaining a trained pre-trained language model by jointly training the pre-trained language model and the plurality of task models according to the typography structure information and the text information; and generating a target pre-trained language model by fine-tuning the trained pre-trained language model according to the typography structure information and the text information.

Method for generating pre-trained language model, electronic device and storage medium

A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Method and apparatus for generating temporal knowledge graph, device, and medium

The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video.

Video classification method, electronic device and storage medium

Provided are a method and system for controlling interactive live streaming co-hosting, a device, and a medium. The method for controlling interactive live streaming co-hosting includes acquiring a first interactive image and receiving a mirror interactive image of a second terminal by a first terminal, where the first terminal and the second terminal are in a live streaming co-hosting state, and the mirror interactive image of the second terminal is obtained by performing mirror processing on a second interactive image of the second terminal; and combining the first interactive image and the mirror interactive image by the first terminal to obtain a target interactive image.

Method and system for controlling interactive live streaming co-hosting, device, and medium

The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

Representation learning method and device based on natural language and knowledge graph

The present disclosure discloses a method and an apparatus for generating a vector representation of a knowledge graph, and relates to a field of a field of artificial intelligence technologies. The detailed implementing solution is: obtaining a knowledge graph, the knowledge graph including a plurality of entity nodes; obtaining a context type and context data corresponding to the knowledge graph; and generating vector representations corresponding to the plurality of entity nodes by a context model based on the context data and the context type.

Method and apparatus for generating vector representation of knowledge graph

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Beijing, China

Yong Zhu