Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Sandeep Tata"'
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Publikováno v:
Proceedings of the VLDB Endowment. 14:997-1005
Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out
Publikováno v:
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining.
Publikováno v:
KDD
Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, p
Publikováno v:
Proceedings of the VLDB Endowment. 12:1235-1248
In emails, information abounds. Whether it be a bill reminder, a hotel confirmation, or a shipping notification, our emails contain useful bits of information that enable a number of applications. Most of this email traffic is machine-generated, sent
Publikováno v:
KDD
Extracting structured data from HTML documents is a long-studied problem with a broad range of applications like augmenting knowledge bases, supporting faceted search, and providing domain-specific experiences for key verticals like shopping and movi
Autor:
Brian Calaci, Zhen Qin, Ryan Evans, Sandeep Tata, Michael Rose, Sean Abraham, Zac Wilson, Suming J. Chen, Michael Colagrosso, Donald Metzler
Publikováno v:
KDD
Quick Access is a machine-learned system in Google Drive that predicts which files a user wants to open. Adding Quick Access recommendations to the Drive homepage cut the amount of time that users spend locating their files in half. Aggregated over t
Autor:
Marc Najork, Navneet Potti, Qi Zhao, James B. Wendt, Bodhisattwa Prasad Majumder, Sandeep Tata
Publikováno v:
ACL
We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate
Autor:
Marc Najork, Nguyen Vo, Qi Zhao, Sandeep Tata, Furkan Kocayusufoglu, Ying Sheng, James B. Wendt
Publikováno v:
WWW
Recent studies show that an overwhelming majority of emails are machine-generated and sent by businesses to consumers. Many large email services are interested in extracting structured data from such emails to enable intelligent assistants. This allo
Publikováno v:
KDD
Extracting structured data from emails can enable several assistive experiences, such as reminding the user when a bill payment is due, answering queries about the departure time of a booked flight, or proactively surfacing an emailed discount coupon