LATENT USER COMMUNITIES
    1.
    发明申请

    公开(公告)号:US20180253485A1

    公开(公告)日:2018-09-06

    申请号:US15446913

    申请日:2017-03-01

    Applicant: Yahoo! Inc.

    Abstract: A method implemented by at least one server computer is provided, including: providing, over the Internet, access to a plurality of topics, wherein each topic includes, and further provides access to, a plurality of posted items; recording interaction data for the plurality of topics, the interaction data identifying user activity occurring within each of the topics; analyzing the interaction data to identify clusters of topics that exhibit similar behavioral patterns; for each cluster of topics, generating a community that includes the topics in the cluster; providing, over the Internet, access to the communities, wherein accessing a given community further provides access to the topics included in that community, which further provide access to the posted items that are included in the topics within that community.

    Location-Based Recommendations Using Nearest Neighbors in a Locality Sensitive Hashing (LSH) Index

    公开(公告)号:US20170147575A1

    公开(公告)日:2017-05-25

    申请号:US14948213

    申请日:2015-11-20

    Applicant: Yahoo! Inc.

    Abstract: Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface (GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.

    Entity disambiguation
    4.
    发明授权

    公开(公告)号:US11907858B2

    公开(公告)日:2024-02-20

    申请号:US15425978

    申请日:2017-02-06

    Applicant: Yahoo!, Inc.

    CPC classification number: G06N5/04 G06F16/36

    Abstract: One or more computing devices, systems, and/or methods for entity disambiguation are provided. For example, a document may be analyzed to identify a first mention and a second mention. One or more techniques may be used to select and link a candidate entity, from a first set of candidate entities, to the first mention and select and link a candidate entity, from a second set of candidate entities, to the second mention.

    ENTITY DISAMBIGUATION
    5.
    发明申请

    公开(公告)号:US20180225576A1

    公开(公告)日:2018-08-09

    申请号:US15425978

    申请日:2017-02-06

    Applicant: Yahoo!, Inc.

    CPC classification number: G06N5/04 G06F16/36

    Abstract: One or more computing devices, systems, and/or methods for entity disambiguation are provided. For example, a document may be analyzed to identify a first mention and a second mention. One or more techniques may be used to select and link a candidate entity, from a first set of candidate entities, to the first mention and select and link a candidate entity, from a second set of candidate entities, to the second mention.

    Identifying Constructive Sub-Dialogues
    6.
    发明申请

    公开(公告)号:US20180203846A1

    公开(公告)日:2018-07-19

    申请号:US15406565

    申请日:2017-01-13

    Applicant: Yahoo! Inc.

    Abstract: Software on a website hosting an online forum extracts a plurality of sub-dialogues from each thread in a corpus from the online forum. The software obtains one or more sub-dialogue annotations associated with each sub-dialogue. The sub-dialogue annotations include an annotation as to whether the sub-dialogue is constructive. The software extracts a plurality of features from each sub-dialogue uses them and the sub-dialogue annotations associated with the sub-dialogue to train a classifier that determines whether a particular sub-dialogue is constructive. Then the software obtains a new sub-dialogue from a thread currently displayed in the online forum and extracts the plurality of features from the new sub-dialogue. The software inputs the features extracted from the new sub-dialogue into the classifier and obtains a determination as to whether the new sub-dialogue is constructive.

    Location-based recommendations using nearest neighbors in a locality sensitive hashing (LSH) index

    公开(公告)号:US10521413B2

    公开(公告)日:2019-12-31

    申请号:US14948213

    申请日:2015-11-20

    Applicant: Yahoo! Inc.

    Abstract: Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface (GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.

    MULTILABEL LEARNING VIA SUPERVISED JOINT EMBEDDING OF DOCUMENTS AND LABELS

    公开(公告)号:US20180285459A1

    公开(公告)日:2018-10-04

    申请号:US15471455

    申请日:2017-03-28

    Applicant: Yahoo! Inc.

    Abstract: A method implemented by at least one server computer is provided, including the following operations: receiving a plurality of training documents, each training document being defined by a sequence of words, each training document having one or more labels associated therewith; embedding the training documents, the words, and the labels in a vector space, wherein the embedding is configured to locate a given training document and its associated labels in proximity to each other in the vector space; embedding a new document in the vector space; performing a proximity search in the vector space to identify a set of nearest labels to the new document in the vector space; associating the nearest labels to the new document.

    Scalable Multilingual Named-Entity Recognition

    公开(公告)号:US20180203843A1

    公开(公告)日:2018-07-19

    申请号:US15406586

    申请日:2017-01-13

    Applicant: Yahoo! Inc.

    Abstract: Software on a website serves a user of an online content aggregation service a first article that the user views. The software extracts named entities from the first article using a named-entity recognizer. The named-entity recognizer uses a sequence of word embeddings as inputs to a conditional random field (CRF) tool to assign labels to each of the word embeddings. Each of the word embeddings is associated with a word in the first article and is trained using an entire topical article from a corpus of topical articles as a context for the word. The software then creates rankings for articles ingested by the content aggregation service based at least in part on the named entities and serves the user a second article using the rankings.

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