-
公开(公告)号：US11960573B1
公开(公告)日：2024-04-16
申请号：US17981822
申请日：2022-11-07
发明人： Tianchuan Du , Keng-Hao Chang , Ruofei Zhang , Paul Liu
IPC分类号： G06K9/62 , G06F17/16 , G06F18/20 , G06F18/241 , G06F18/25 , G06F40/30 , G06N3/045 , G06N3/084 , G06N5/046 , G06N20/10 , G06N20/20
CPC分类号： G06F18/241 , G06F17/16 , G06F18/251 , G06F18/29 , G06F40/30 , G06N3/045 , G06N3/084 , G06N5/046 , G06N20/10 , G06N20/20
摘要： Neural network-based categorization can be improved by incorporating graph neural networks that operate on a graph representing the taxonomy of the categories into which a given input is to be categorized by the neural network based-categorization. The output of a graph neural network, operating on a graph representing the taxonomy of categories, can be combined with the output of a neural network operating upon the input to be categorized, such as through an interaction of multidimensional output data, such as a dot product of output vectors. In such a manner, information conveying the explicit relationships between categories, as defined by the taxonomy, can be incorporated into the categorization. To recapture information, incorporate new information, or reemphasize information a second neural network can also operate upon the input to be categorized, with the output of such a second neural network being merged with the output of the interaction.
-
公开(公告)号：US11921766B2
公开(公告)日：2024-03-05
申请号：US17901885
申请日：2022-09-02
发明人： Keng-hao Chang , Ruofei Zhang , John Weston Hughes
CPC分类号： G06F16/345 , G06F16/3347 , G06N3/08
摘要： Described herein are technologies related to constructing supplemental content items that summarize electronic landing pages. A sequence to sequence model that is configured to construct supplemental content items is trained based upon a corpus of electronic landing pages and supplemental content items that have been constructed by domain experts, wherein each landing page has a respective supplemental content item assigned thereto. The sequence to sequence model is additionally trained using self critical sequence training, where estimated click through rates of supplemental content items generated by the sequence to sequence model are employed to train the sequence to sequence model.
-
公开(公告)号：US10654380B2
公开(公告)日：2020-05-19
申请号：US15612555
申请日：2017-06-02
发明人： Keng-hao Chang , Ruofei Zhang , Zi Yin
摘要： The present application describes a system and method for converting a natural language query to a standard query using a sequence-to-sequence neural network. As described herein, when a natural language query is receive, the natural language query is converted to a standard query using a sequence-to-sequence model. In some cases, the sequence-to-sequence model is associated with an attention layer. A search using the standard query is performed and various documents may be returned. The documents that result from the search are scored based, at least in part, on a determined conditional entropy of the document. The conditional entropy is determined using the natural language query and the document.
-
公开(公告)号：US11603017B2
公开(公告)日：2023-03-14
申请号：US16877410
申请日：2020-05-18
发明人： Keng-hao Chang , Ruofei Zhang , Zi Yin
摘要： The present application describes a system and method for converting a natural language query to a standard query using a sequence-to-sequence neural network. As described herein, when a natural language query is receive, the natural language query is converted to a standard query using a sequence-to-sequence model. In some cases, the sequence-to-sequence model is associated with an attention layer. A search using the standard query is performed and various documents may be returned. The documents that result from the search are scored based, at least in part, on a determined conditional entropy of the document. The conditional entropy is determined using the natural language query and the document.
-
公开(公告)号：US10354182B2
公开(公告)日：2019-07-16
申请号：US14926617
申请日：2015-10-29
发明人： Keng-hao Chang , Ruofei Zhang , Shuangfei Zhai
IPC分类号： G06N3/04 , G06N3/08 , G06F16/33 , G06F17/27 , G06F16/951
摘要： A computer-implemented technique is described herein for identifying one or more content items that are relevant to an input linguistic item (e.g., an input query) using a deep-structured neural network, trained based on a corpus of click-through data. The input linguistic item has a collection of input tokens. The deep-structured neural network includes a first part that produces word embeddings associated with the respective input tokens, a second part that generates state vectors that capture context information associated with the input tokens, and a third part which distinguishes important parts of the input linguistic item from less important parts. The second part of the deep-structured neural network can be implemented as a recurrent neural network, such as a bi-directional neural network. The third part of the deep-structured neural network can generate a concept vector by forming a weighted sum of the state vectors.
-
公开(公告)号：US20170091814A1
公开(公告)日：2017-03-30
申请号：US14866710
申请日：2015-09-25
发明人： Pengqi Liu , Ruofei Zhang
CPC分类号： G06Q30/0255 , G06F17/3066 , G06F17/30867 , G06Q30/0269
摘要： A computer-implemented technique is described herein for shortening an original query into one or more sub-queries. The technique chooses the sub-query(ies) such that they preserve the original intent of the original query. To accomplish this goal, the technique uses graph-based analysis to generate a set of richly descriptive query-context-specific feature values for each sub-query, and then uses those feature values to score the relevance of that sub-query.
-
7.发明授权公开(公告)号：US11676001B2
公开(公告)日：2023-06-13
申请号：US17093426
申请日：2020-11-09
发明人： Jian Jiao , Xiaodong Liu , Ruofei Zhang , Jianfeng Gao
IPC分类号： G06N3/045
CPC分类号： G06N3/045
摘要： Knowledge graphs can greatly improve the quality of content recommendation systems. There is a broad variety of knowledge graphs in the domain including clicked user-ad graphs, clicked query-ad graphs, keyword-display URL graphs etc. A hierarchical Transformer model learns entity embeddings in knowledge graphs. The model consists of two different Transformer blocks where the bottom block generates relation-dependent embeddings for the source entity and its neighbors, and the top block aggregates the outputs from the bottom block to produce the target entity embedding. To balance the information from contextual entities and the source entity itself, a masked entity model (MEM) task is combined with a link prediction task in model training.
-
公开(公告)号：US11250042B2
公开(公告)日：2022-02-15
申请号：US16001495
申请日：2018-06-06
IPC分类号： G06F16/35 , G06F16/31 , G06K9/62 , G06F40/30 , G06F40/247
摘要： A taxonomy of categories, attributes, and values can be conflated with new data triplets by identifying one or more conflation candidates among the attribute-value pairs within a category of the taxonomy that matches the category of the data triplet, and determining a suitable merge action for conflating the data triplet with each conflation candidate. The task of determining merge actions may be cast as a classification problem, and may be solved by an ensemble classifier.
-
9.发明授权公开(公告)号：US11163940B2
公开(公告)日：2021-11-02
申请号：US16422992
申请日：2019-05-25
发明人： Qun Li , Changbo Hu , Keng-hao Chang , Ruofei Zhang
IPC分类号： G06F17/00 , G06F40/166 , G06F16/33 , G06K9/00 , G06N3/02
摘要： Technologies are described herein that relate to identifying supplemental content items that are related to objects captured in images of webpages. A computing system receives an indication that a client computing device has a webpage displayed thereon that includes an image. The image is provided to a first DNN that is configured to identify a portion of the image that includes an object of a type from amongst a plurality of predefined types. Once the portion of the image is identified, the portion of the image is provided to a plurality of DNNs, with each of the DNNs configured to output a word or phrase that represents a value of a respective attribute of the object. A sequence of words or phrases output by the plurality of DNNs is provided to a search computing system, which identifies a supplemental content item based upon the sequence of words or phrases.
-
公开(公告)号：US10459928B2
公开(公告)日：2019-10-29
申请号：US15379262
申请日：2016-12-14
发明人： Keng-hao Chang , Ruofei Zhang , Zi Yin
IPC分类号： G06F16/00 , G06F16/2457 , G06F16/93 , G06N3/04 , G06F16/33 , G06F16/332
摘要： A technique of scoring a query against a document using sequence to sequence neural networks. The technique comprises: receiving a query comprising a plurality of words from a user; performing a search for a document comprising words based on the query; feeding the words of the document as the input of an encoder of a multilayer sequence to sequence converter; generating a plurality of vectors at a decoder of the multilayer sequence to sequence converter, each vector comprising a probability associated with a respective word in the query; looking up in the respective vector each word's probability of being associated with the document; multiplying every word's probability together to determine an overall probability of the query being associated with the document; and returning the document to the user if the overall probability of the query being associated with the document is greater than a threshold value.
-
-
-
-
-
-
-
-
-
搜索结果
25 条记录 (耗时 0.032 秒)
