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公开(公告)号：US20180189260A1
公开(公告)日：2018-07-05
申请号：US15395687
申请日：2016-12-30
Applicant: Facebook, Inc.
Inventor： Anitha Kannan , Yuandong Tian , Yann Nicolas Dauphin
CPC classification number: G06F17/276 , G06N3/0445 , G06N3/08 , H04L51/32
Abstract: Systems, methods, and non-transitory computer-readable media can train a sequence model to output respective captions, or portions of captions, for content items. A determination can be made that a user of the social networking system is posting a content item for publication through a social networking system. A set of captions, or portions of captions, can be determined for the content item being posted based at least in part on the sequence model. The set of captions, or portions of captions, can be provided as suggestions to the user for use in a caption describing the content item being posted.
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公开(公告)号：US20180336457A1
公开(公告)日：2018-11-22
申请号：US15597290
申请日：2017-05-17
Applicant: Facebook, Inc.
Inventor： Aditya Pal , Deepayan Chakrabarti , Karthik Subbian , Anitha Kannan
Abstract: In one embodiment, a system may access a graph data structure that includes nodes and connections between the nodes. Each node may be associated with a user; each connection between two nodes may represent a relationship between the associated users; and each node may be either labeled or unlabeled with respect to a label type. For each labeled node, a label of the label type of that labeled node may be propagated to other nodes through the connections. For each node, the system may store a label distribution information associated with the label type based on the propagated labels reaching the node. The system may train a machine-learning model using the labels and the label distribution information of a set of the labeled nodes. A predicted label for each unlabeled node may be generated using the model and the label distribution information of the unlabeled node.
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公开(公告)号：US10922609B2
公开(公告)日：2021-02-16
申请号：US15597290
申请日：2017-05-17
Applicant: Facebook, Inc.
Inventor： Aditya Pal , Deepayan Chakrabarti , Karthik Subbian , Anitha Kannan
IPC: G06N3/08 , G06F16/901 , G06N3/04 , G06N5/02 , G06N20/10
Abstract: In one embodiment, a system may access a graph data structure that includes nodes and connections between the nodes. Each node may be associated with a user; each connection between two nodes may represent a relationship between the associated users; and each node may be either labeled or unlabeled with respect to a label type. For each labeled node, a label of the label type of that labeled node may be propagated to other nodes through the connections. For each node, the system may store a label distribution information associated with the label type based on the propagated labels reaching the node. The system may train a machine-learning model using the labels and the label distribution information of a set of the labeled nodes. A predicted label for each unlabeled node may be generated using the model and the label distribution information of the unlabeled node.
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公开(公告)号：US20180197098A1
公开(公告)日：2018-07-12
申请号：US15403107
申请日：2017-01-10
Applicant: Facebook, Inc.
Inventor： Karthik Subbian , Anitha Kannan , Yann Nicolas Dauphin
CPC classification number: G06N7/005 , G06N3/0454 , G06Q10/10 , G06Q30/02 , G06Q30/06 , G06Q50/01 , H04L51/10 , H04L51/32
Abstract: Systems, methods, and non-transitory computer-readable media can determine one or more chunks for a content item to be captioned. Each chunk can include one or more terms that describe at least a portion of the subject matter captured in the content item. One or more sentiments are determined based on the subject matter captured in the content item. One or more emotions are determined for the content item. At least one emoted caption is generated for the content item based at least in part on the one or more chunks, sentiments, and emotions. The emoted caption can include at least one term that conveys an emotion represented by the subject matter captured in the content item.
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