公开(公告)号：US20190251164A1

公开(公告)日：2019-08-15

申请号：US16242365

申请日：2019-01-08

申请人： Lei DING ,  Yixuan TONG ,  Bin DONG ,  Shanshan JIANG ,  Yongwei ZHANG

发明人： Lei DING ,  Yixuan TONG ,  Bin DONG ,  Shanshan JIANG ,  Yongwei ZHANG

IPC分类号： G06F17/27 ,  G06K9/62 ,  G06F16/31 ,  G06F16/35

CPC分类号： G06F17/2765 ,  G06F16/313 ,  G06F16/353 ,  G06K9/6256

摘要： A method, an apparatus and an electronic device for performing entity linking, and a non-transitory computer-readable recording medium are provided. The method includes constructing training data including a plurality of sets of labeled data using an existing unambiguous entity database where unambiguous entities corresponding to respective entity words are stored, each set of the labeled data including a text having an entity word and an unambiguous entity linked with the entity word; training an unambiguous entity recognition model whose output is a matching probability between an entity word in a text and an unambiguous entity using the training data; and inputting a text having an entity word to be recognized into the unambiguous entity recognition model, and determining an unambiguous entity linked with the entity word to be recognized based on an output result of the unambiguous entity recognition model.

公开(公告)号：US20210027178A1

公开(公告)日：2021-01-28

申请号：US16934112

申请日：2020-07-21

申请人： Lei DING ,  Yixuan TONG ,  Bin DONG ,  Shanshan JIANG ,  Yongwei ZHANG

发明人： Lei DING ,  Yixuan TONG ,  Bin DONG ,  Shanshan JIANG ,  Yongwei ZHANG

IPC分类号： G06N5/04 ,  G06F16/9535 ,  G06K9/62 ,  G06N3/04 ,  G06F40/289

摘要： A recommendation method and a recommendation apparatus based on deep reinforcement learning, and a non-transitory computer-readable recording medium are provided. In the method, entity semantic information representation vectors of products are generated based on a product knowledge graph; browsing context information representation vectors of the products are generated based on historical browsing behavior of a user with respect to products; the entity semantic information representation vectors and the browsing context information representation vectors of the respective products are merged to obtain vectors of the products; a recommendation model based on deep reinforcement learning is constructed, and the recommendation model based on the deep reinforcement learning is offline-trained using historical behavior data of the user to obtain the offline-trained recommendation model, the products in the historical behavior data of the user are represented by the vectors of the products; and products are online-recommended using the offline-trained recommendation model.

公开(公告)号：US20210081788A1

公开(公告)日：2021-03-18

申请号：US17015560

申请日：2020-09-09

申请人： Lei DING ,  Yixuan TONG ,  Jiashi ZHANG ,  Shanshan JIANG ,  Yongwei ZHANG

发明人： Lei DING ,  Yixuan TONG ,  Jiashi ZHANG ,  Shanshan JIANG ,  Yongwei ZHANG

IPC分类号： G06N3/08 ,  G06N7/00

摘要： A method and an apparatus for generating sample data, and a non-transitory computer-readable recording medium are provided. In the method, at least two weak supervision recommendation models of a recommendation system are generated; a dependency relation between the at least two weak supervision recommendation models is learned by training a neural network model; and the sample data is re-labelled using the trained neural network model to obtain updated sample data.

公开(公告)号：US20220164536A1

公开(公告)日：2022-05-26

申请号：US17455967

申请日：2021-11-22

申请人： Yixuan TONG ,  Yongwei ZHANG ,  Bin DONG ,  Shanshan JIANG ,  Jiashi ZHANG

发明人： Yixuan TONG ,  Yongwei ZHANG ,  Bin DONG ,  Shanshan JIANG ,  Jiashi ZHANG

IPC分类号： G06F40/295

摘要： A method and an apparatus for sequence labeling on an entity text, and a non-transitory computer-readable recording medium are provided. In the method, a start position of an entity text within a target text is determined. Then, a first matrix is generated based on the start position of the entity text. Elements in the first matrix indicates focusable weights of each word with respect to other words in the target text. Then, a named entity recognition model is generated using the first matrix. The named entity recognition model is obtained by training using first training data, the first training data includes word embeddings corresponding to respective texts in a training text set, and the texts are texts whose entity label has been labeled. Then, the target text is input to the named entity recognition model, and probability distribution of the entity label is output.

公开(公告)号：US20210303777A1

公开(公告)日：2021-09-30

申请号：US17215068

申请日：2021-03-29

申请人： Yixuan TONG ,  Yongwei ZHANG ,  Bin DONG ,  Shanshan JIANG ,  Jiashi ZHANG

发明人： Yixuan TONG ,  Yongwei ZHANG ,  Bin DONG ,  Shanshan JIANG ,  Jiashi ZHANG

IPC分类号： G06F40/166 ,  G06K9/62 ,  G06N3/08

摘要： A method and an apparatus for fusing position information, and a non-transitory computer-readable recording medium are provided. In the method, words of an input sentence are segmented to obtain a first sequence of words in the input sentence, and absolute position information of the words in the first sequence is generated. Then, subwords of the words in the first sequence are segmented to obtain a second sequence including subwords, and position information of the subwords in the second sequence are generated, based on the absolute position information of the words in the first sequence, to which the respective subwords belong. Then, the position information of the subwords in the second sequence are fused into a self-attention model to perform model training or model prediction.

公开(公告)号：US20230394240A1

公开(公告)日：2023-12-07

申请号：US18326292

申请日：2023-05-31

申请人： Yongwei ZHANG ,  Bin Dong ,  Shanshan Jiang ,  Lei Ding ,  Jiashi Zhang

发明人： Yongwei ZHANG ,  Bin Dong ,  Shanshan Jiang ,  Lei Ding ,  Jiashi Zhang

IPC分类号： G06F40/295 ,  G06F40/40

CPC分类号： G06F40/295 ,  G06F40/40

摘要： A method and an apparatus for named entity recognition, and a non-transitory computer-readable recording medium are provided. In the method, text elements are traversed according to a text span to obtain candidate entity words. Then, a class to which the candidate entity word belongs is recognized. The recognizing of the class includes generating a prompt template corresponding to the candidate entity word, and concatenating the text to be recognized and the prompt template to obtain a concatenated text; generating vector representations of the text elements in the concatenated text; generating the vector representation of the candidate entity word according to the vector representations of the text elements of each candidate entity word in the concatenated text, and the vector representation of the text element of the mask word; and classifying the vector representation of the candidate entity word to obtain the class of the candidate entity word.