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pro vyhledávání: '"Amsterdamer, Yael"'
Autor:
Amarilli, Antoine, Amsterdamer, Yael
In recent work, we have introduced a framework for fine-grained consent management in databases, which combines Boolean data provenance with the field of interactive Boolean evaluation. In turn, interactive Boolean evaluation aims at unveiling the un
Externí odkaz:
http://arxiv.org/abs/2205.04224
We present a framework for creating small, informative sub-tables of large data tables to facilitate the first step of data science: data exploration. Given a large data table table T, the goal is to create a sub-table of small, fixed dimensions, by
Externí odkaz:
http://arxiv.org/abs/2203.02754
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications. In contrast, multi-document keyphrase extraction has been infrequently studied, despite its utility for
Externí odkaz:
http://arxiv.org/abs/2110.01073
Autor:
Shapira, Ori, Pasunuru, Ramakanth, Ronen, Hadar, Bansal, Mohit, Amsterdamer, Yael, Dagan, Ido
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly di
Externí odkaz:
http://arxiv.org/abs/2009.08380
Autor:
Shapira, Ori, Gabay, David, Gao, Yang, Ronen, Hadar, Pasunuru, Ramakanth, Bansal, Mohit, Amsterdamer, Yael, Dagan, Ido
Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objectiv
Externí odkaz:
http://arxiv.org/abs/1904.05929
Many practical scenarios make it necessary to evaluate top-k queries over data items with partially unknown values. This paper considers a setting where the values are taken from a numerical domain, and where some partial order constraints are given
Externí odkaz:
http://arxiv.org/abs/1701.02634
Akademický článek
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Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are
Externí odkaz:
http://arxiv.org/abs/1403.0783
We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the
Externí odkaz:
http://arxiv.org/abs/1312.3248
Autor:
Amsterdamer, Yael, Davidson, Susan B., Deutch, Daniel, Milo, Tova, Stoyanovich, Julia, Tannen, Val
Publikováno v:
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 4, pp. 346-357 (2011)
Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs (coarse-grained dependencies). Furthermore, it does not model the internal state of a module, which can change between repeated executi
Externí odkaz:
http://arxiv.org/abs/1201.0231