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Evfrosiniya Zerminova

dblp:255/5037 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Data mining · 67% Machine learning and data management · 33%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning and data management
data annotation
0.922020
Practice of Efficient Data Collection via Crowdsourcing: Aggregation, Incremental Relabelling, and Pricing · WSDM 2020
Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing · SIGMOD Conference 2020
Data mining › crowdsourcing
label aggregation
0.922020
Practice of Efficient Data Collection via Crowdsourcing: Aggregation, Incremental Relabelling, and Pricing · WSDM 2020
Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing · SIGMOD Conference 2020
Data mining › crowdsourcing
crowdsourced annotation
0.412020
Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing · SIGMOD Conference 2020
Data mining › crowdsourcing
crowdsourced data
0.412020
Practice of Efficient Data Collection via Crowdsourcing: Aggregation, Incremental Relabelling, and Pricing · WSDM 2020

Methods — techniques the papers use, named apart from their topics

crowdsourcing · 0.4
YearPublicationVenuePosition
2020 Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing
abstract
In this tutorial, we present a portion of unique industry experience in efficient data labeling via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via public crowdsourcing marketplaces and will present the key components of efficient label collection. This will be followed by a practice session, where participants will choose one of the real label collection tasks, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. While the crowd performers are annotating the project set up by the attendees, we will present the major theoretical results in efficient aggregation, incremental relabeling, and dynamic pricing. We will also discuss their strengths and weaknesses as well as applicability to real-world tasks, summarizing our five year-long research and industrial expertise in crowdsourcing. Finally, participants will receive a feedback about their projects and practical advice on how to make them more efficient. We invite beginners, advanced specialists, and researchers to learn how to collect high quality labeled data and do it efficiently.
Alexey Drutsa, Valentina Fedorova, Dmitry Ustalov, Olga Megorskaya, Evfrosiniya Zerminova, Daria Baidakova
SIGMOD Conference5
2020 Practice of Efficient Data Collection via Crowdsourcing: Aggregation, Incremental Relabelling, and Pricing
abstract
In this tutorial, we present a portion of unique industry experience in efficient data labelling via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labelling via public crowdsourcing marketplaces and will present key components of efficient label collection. This will be followed by a practice session, where participants will choose one of the real label collection tasks, experiment with selecting settings for the labelling process, and launch their label collection project on Yandex.Toloka, one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. Finally, participants will receive a feedback about their projects and practical advice to make them more efficient. We expect that our tutorial will address an audience with a wide range of background and interests. We do not require specific prerequisite knowledge or skills. We invite beginners, advanced specialists, and researchers to learn how to efficiently collect labelled data.
Alexey Drutsa, Valentina Fedorova, Dmitry Ustalov, Olga Megorskaya, Evfrosiniya Zerminova, Daria Baidakova
WSDM5