Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Pallika Kanani"'
Autor:
Laura Dietz, Bhaskar Mitra, Jeremy Pickens, Hana Anber, Sandeep Avula, Asia Biega, Adrian Boteanu, Shubham Chatterjee, Jeff Dalton, Shiri Dori-Hacohen, John Foley, Henry Feild, Ben Gamari, Rosie Jones, Pallika Kanani, Sumanta Kashyapi, Widad Machmouchi, Matthew Mitsui, Steve Nole, Alexandre Tachard Passos, Jordan Ramsdell, Adam Roegiest, David Smith, Alessandro Sordoni
Publikováno v:
ACM SIGIR Forum. 53:62-75
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first iteration of this vision materialized in the form of a three day workshop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::782cc629721c628bc77967a5c1371efa
https://doi.org/10.1145/3458553.3458560
https://doi.org/10.1145/3458553.3458560
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030937324
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bc24eed98099df31af24d2f52b899cd
https://doi.org/10.1007/978-3-030-93733-1_37
https://doi.org/10.1007/978-3-030-93733-1_37
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 30
We present a method that consumes a large corpus of multilingual text and produces a single, unified word embedding in which the word vectors generalize across languages. In contrast to current approaches that require language identification, our met
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff0b1e18f41131b0beb8952d3ff47162
https://doi.org/10.1609/aaai.v30i1.10360
https://doi.org/10.1609/aaai.v30i1.10360
Autor:
Andrew McCallum, Pallika Kanani
Publikováno v:
WSDM
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to do so under resource limitations. We formulate the information gathering t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83e611a4f6e6c573647e92ceb4c1824e
https://doi.org/10.1145/2124295.2124328
https://doi.org/10.1145/2124295.2124328
Publikováno v:
SIGIR
Ranking search results is a fundamental problem in information retrieval. In this paper we explore whether the use of proximity and phrase information can improve web retrieval accuracy. We build on existing research by incorporating novel ranking fe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb3d16fdc2e741136709fec5ada49b5f
https://doi.org/10.1145/1835449.1835477
https://doi.org/10.1145/1835449.1835477
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783642136566
PAKDD (1)
PAKDD (1)
We present a general framework for the task of extracting specific information “on demand” from a large corpus such as the Web under resource-constraints. Given a database with missing or uncertain information, the proposed system automatically f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14c19ef1de7dbb5db9f5531357109ec7
https://doi.org/10.1007/978-3-642-13657-3_45
https://doi.org/10.1007/978-3-642-13657-3_45
Autor:
Andrew McCallum, Pallika Kanani
Publikováno v:
Learning Theory ISBN: 9783540729259
COLT
COLT
We present a new class of problems, called resource-bounded information gathering for correlation clustering. Our goal is to perform correlation clustering under circumstances in which accuracy may be improved by augmenting the given graph with addit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3944d3d5eb0b43e659148b01d36ef09f
https://doi.org/10.1007/978-3-540-72927-3_46
https://doi.org/10.1007/978-3-540-72927-3_46