Yahoo Assets LLC

Oath Inc.

Verizon Media Inc.

Yahoo Ad Tech LLC

Kapil Raj Thadani

Yashar Mehdad

Amanda Joy Stent

Aasish Kumar Pappu

Dragomir Radkov Radev

Akshay Soni

Troy Chevalier

Parikshit Shah

Nasser Zalmout

Zhi Qu

Aaron Nagao

Sreekanth Ramakrishnan

Youssef Billawala

Roi Blanco

Arunkumar Balasubramanian

Sanika Shirwadkar

Xingyue Zhou

Yueh-Ning Ku

Andrew Jeremy Crews

Praveen Mareedu

Benjamin J Culpepper

Lakshmi V Kesiraju

Alex J Shaw

Amanda Stent

Philip Anthony Hairr

Xi Gao

Lippe Oosterhof

Youssef Billawal

One or more computing devices, systems, and/or methods for generating a video in a target language are provided. In an example, a first video, in which a first speaker speaks in a first language, is identified. A translated transcript in a second language is determined. The translated transcript is indicative of a translation of speech spoken by the first speaker in the first video. Based upon the translated transcript and a speaker profile associated with a second speaker, first audio, including an auditory representation of the translated transcript being spoken in a voice of the second speaker, is generated. Based upon the first video and the first audio, a second video, in which mouth movements of the first speaker are aligned with speech of the auditory representation of the first audio, is generated.

System and method for generating video in target language

One or more computing devices, systems, and/or methods for generating summary lists based upon articles are presented. In an example, a summarizing set of sentences of an article may be identified. The summarizing set of sentences may be analyzed to identify one or more first sentences of the summarizing set of sentences that meet a set of conditions and/or identify one or more second sentences of the summarizing set of sentences that do not meet the set of conditions. A summary list summarizing the article may be generated based upon the one or more first sentences.

Generation and presentation of summary list based upon article

A method is provided, including: storing comments generated in response to a content item served over a network; analyzing the comments to determine features associated with each of the comments; using a scoring model to score each comment based on the comment's corresponding features; receiving a request to serve a subset of the comments; responsive to the request, selecting a ranking of the comments that is one permutation from possible rankings of the comments, wherein selecting the ranking is in accordance with a probability distribution of the possible rankings that is based on the scores of the comments; serving comments identified by the selected ranking over the network to a client device; determining a dwell time on the served comments; applying the dwell time to update the scoring model.

Ranking user comments on media using reinforcement learning optimizing for session dwell time

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

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

One or more computing devices, systems, and/or methods for generating presentations based upon articles are presented. For example, an article may be selected from one or more article databases. Content items may be extracted from the article, wherein the content items comprise one or more videos, one or more images and/or one or more social media posts. Text of the article may be analyzed to generate a plurality of text segments. A presentation, comprising a plurality of slides, may be generated based upon the content items and the plurality of text segments. A graphical user interface of a client device may be controlled to display a presentation editing interface comprising a representation of the presentation. One or more inputs, corresponding to one or more edits to the presentation, may be received via the presentation editing interface. An edited presentation may be generated based upon the one or more inputs.

Generating presentations based upon articles

Systems, methods, and apparatuses are disclosed for adaptively generating a summary of web-based content based on an attribute of a mobile communication device having transmitted a request for the web-based content. By adaptively generating the summary based on an attribute of the mobile communication device such as an amount of visual space available or a number of characters permitted in the interface, a display of the web-based content may be controlled on the mobile communication device in a way that was not previously available. This enables control of displaying web-based content that has been adaptively generated to be displayed on limited display screens based on a learned attribute of the mobile communication device requesting the web-based content.

Scalable and effective document summarization framework

Growing community of inventors

New York, NY, United States of America

Kapil Raj Thadani