@inproceedings{jukic-etal-2023-alanno,
title = "{ALANNO}: An Active Learning Annotation System for Mortals",
author = "Juki{\'c}, Josip and
Jeleni{\'c}, Fran and
Bi{\'c}ani{\'c}, Miroslav and
Snajder, Jan",
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.26",
doi = "10.18653/v1/2023.eacl-demo.26",
pages = "228--235",
abstract = "Supervised machine learning has become the cornerstone of today{'}s data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) {--} a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.",
}
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<abstract>Supervised machine learning has become the cornerstone of today’s data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) – a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.</abstract>
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%0 Conference Proceedings
%T ALANNO: An Active Learning Annotation System for Mortals
%A Jukić, Josip
%A Jelenić, Fran
%A Bićanić, Miroslav
%A Snajder, Jan
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F jukic-etal-2023-alanno
%X Supervised machine learning has become the cornerstone of today’s data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) – a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.
%R 10.18653/v1/2023.eacl-demo.26
%U https://aclanthology.org/2023.eacl-demo.26
%U https://doi.org/10.18653/v1/2023.eacl-demo.26
%P 228-235
Markdown (Informal)
[ALANNO: An Active Learning Annotation System for Mortals](https://aclanthology.org/2023.eacl-demo.26) (Jukić et al., EACL 2023)
ACL
- Josip Jukić, Fran Jelenić, Miroslav Bićanić, and Jan Snajder. 2023. ALANNO: An Active Learning Annotation System for Mortals. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 228–235, Dubrovnik, Croatia. Association for Computational Linguistics.