Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Verizon Patent and Licensing Inc.

Spotify Ab

Aasish Kumar Pappu

Amanda Joy Stent

Joel Tetreault

Akshay Soni

Paloma De Juan

Yashar Mehdad

Kapil Raj Thadani

Yoelle Smadja Maarek

Edo Liberty

Troy Chevalier

Meizhu Liu

Xue Wu

Rosemary Ellen Jones

Ellie Pavlick

Sungjin Lee

Liangliang Cao

Gilad Tsur

Nasser Zalmout

Sari Nahmad

Kristin Savage

Aswin Sivaraman

Yifan Hu

Rishabh Mehrotra

Roi Blanco

Lakshmi Narayan Bhamidipati

Mounia Lalmas-Roelleke

Shaunak Mishra

Federico Tomasi

Brian Christian Peter Brost

Hugo Flávio Ventura Galvão

Xiang Li

Amanda Stent

Courtney Napoles Cohen

Vivek Kulkarni

Sheng Chen

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

The present teaching relates to method, system, and programming for responding to an image related query. Information related to each of a plurality of images is received, wherein the information represents concepts co-existing in the image. Visual semantics for each of the plurality of images are created based on the information related thereto. Representations of scenes of the plurality of images are obtained via machine learning, based on the visual semantics of the plurality of images, wherein the representations capture concepts associated with the scenes.

System and method for learning scene embeddings via visual semantics and application thereof

An electronic device obtains a listening history for a media item, the listening history including retention information indicating, for each respective portion of a plurality of portions of the media item, a number of listeners who listened to the respective portion of the media item. The electronic device, using the retention information, determines a pattern indicating a reduction in the number of listeners who listened to corresponding portions of the media item and determines a start time and an end time corresponding to a first portion of the corresponding portions of the media item. In accordance with the determination that the first portion of the corresponding portions of the media item meets predefined sub-content criteria, the electronic device stores an indication that the first portion of the media item comprises first sub-content, different from primary content, embedded in the media item.

Systems and methods for skip-based content detection

Methods, systems and computer program products are provided for query understanding. A non-focused query quantifier generates non-focused query features that quantify a non-focused query and a non-focused query predictor generates a prediction associated with the non-focused query based on the non-focused query features.

Query understanding methods and systems

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.

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

Systems and methods are disclosed for utilizing a comment moderation bot for detecting and normalizing neologisms in social media. One method comprises transmitting, by a neologism normalization system, a comment moderation bot for detecting neologisms on an online platform maintained by one or more publisher systems. The comment moderation bot may aggregate data related to user comments and transmit the aggregated data to the neologism normalization system for further processing. The neologism normalization system implements unsupervised machine learning models for detecting neologisms in the aggregated data through tokenization and filtering; and normalizing the neologisms through similarity analysis and lattice decoding.

Systems and methods for unsupervised neologism normalization of electronic content using embedding space mapping

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.

Latent user communities

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatic creation of a formatted, readable transcript of multimedia content, which is derived, extracted, determined, or otherwise identified from the multimedia content. The formatted, readable transcript can be utilized to increase accuracy and efficiency in search engine optimization, as well as identification of relevant digital content available for communication to a user.

Computerized system and method for formatted transcription of multimedia content

An electronic message composition support system, method and architecture including machine learning and natural language processing techniques for extending message composition capability and support and to provide feedback to a user regarding an error, condition, etc., detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Electronic message composition support method and apparatus

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.

An electronic device associated with a media-providing service obtains listening history for a media item. The listening history includes retention information that indicates a number of listeners who listened to the respective portion of the media item. Using the retention information, the electronic device generates a retention graph that represents the number of listeners who listened to corresponding portions of the media item as a function of time. The electronic device detects an extremum in the retention graph. The extremum corresponds to a reduction in the number of listeners who listened to the corresponding portions of the media item. The electronic device determines whether the extremum meets predefined criteria, and in accordance with the determination that the extremum meets the predefined criteria, the electronic device stores an indication that the portions of the media item corresponding to the extremum include sub-content embedded in the media item.

The present teaching relates to generating user profiles with semantic knowledge. A first information associated with a user is obtained. One or more entities are identified from the first information. The one or more entities are augmented based on second information to generate a set of augmented entities. The set of augmented entities are clustered into a set of hierarchical clusters. A set of user profiles is generated based on the set of hierarchical clusters so that the user profile is to be used to personalize content recommendation.

Method and system for user profiling for content recommendation

One or more computing devices, systems, and/or methods are provided. An informational database may be analyzed based upon an entity to identify an informational article. The informational article may be analyzed to identify one or more first references associated with a first sentiment category and/or one or more second references associated with a second sentiment category. Sentiment tags, indicative of sentiment categories, may be assigned to one or more first reference content items associated with the one or more first references and/or to one or more second reference content items associated with the one or more second references. For each content item of a plurality of content items associated with the entity, a sentiment category associated with the content item may be determined based upon a comparison of the content item with the one or more first reference content items and the one or more second reference content items.

Determining sentiment of content and selecting content items for transmission to devices

One or more computing devices, systems, and/or methods for displaying videos based upon selectable inputs associated with tags are presented. For example, a video may be identified. A transcript, associated with the video, may be determined. The transcript may comprise a plurality of text segments. The transcript may be analyzed to generate a plurality of sets of tags associated with the transcript. A plurality of selectable inputs may be generated based upon the plurality of sets of tags. A video interface, comprising the plurality of selectable inputs, may be displayed on a first device. A selection of a first selectable input may be received via the video interface. The first selectable input may be associated with a first tag of the plurality of sets of tags and a first time of the video. A second device may display the video based upon the first time of the video.

Growing community of inventors

Jersey City, NJ, United States of America

Aasish Kumar Pappu