J
Jay Ponte
Researcher at Google
Publications - 52
Citations - 5016
Jay Ponte is an academic researcher from Google. The author has contributed to research in topics: Web page & Web search query. The author has an hindex of 23, co-authored 52 publications receiving 4932 citations. Previous affiliations of Jay Ponte include Mitre Corporation & Apple Inc..
Papers
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Journal ArticleDOI
A language modeling approach to information retrieval
Jay Ponte,W. Bruce Croft +1 more
TL;DR: It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection and provide further proof of concept for the use of language models for retrieval tasks.
Journal ArticleDOI
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002
James Allan,Jay Aslam,Nicholas J. Belkin,Chris Buckley,Jamie Callan,Bruce Croft,Susan T. Dumais,Norbert Fuhr,Donna Harman,David J. Harper,Djoerd Hiemstra,Thomas Hofmann,Eduard Hovy,Wessel Kraaij,John Lafferty,Victor Lavrenko,David Lewis,Liz Liddy,R. Manmatha,Andrew McCallum,Jay Ponte,John M. Prager,Dragomir R. Radev,Philip Resnik,Stephen Robertson,Roni Rosenfeld,Salim Roukos,Mark Sanderson,R. Schwartz,Amit Singhal,Alan F. Smeaton,Howard R. Turtle,Ellen M. Voorhees,Ralph Weischedel,Jinxi Xu,ChengXiang Zhai +35 more
TL;DR: This report summarizes a discussion of IR research challenges that took place at a recent workshop, which identified Contextual retrieval and global information access were identified as particularly important long-term challenges.
Book ChapterDOI
Text Segmentation by Topic
Jay Ponte,W. Bruce Croft +1 more
TL;DR: This study presents a method for segmentation which makes use of a query expansion technique to find common features for the topic segments and experiments show that it can be effective.
Patent
Data merging techniques
TL;DR: In this article, the authors present a system for performing online data queries, which is a distributed computer system with a plurality of server nodes each fully redundant and capable of processing a user query request.
Proceedings Article
Large Scale Parallel Document Mining for Machine Translation
TL;DR: A distributed system is described that reliably mines parallel text from large corpora as cross-language near-duplicate detection, enabled by an initial, low-quality batch translation.