Reversing uncertainty sampling to improve active learning schemes
- Autores
- Cardellino, Cristian Adrián; Teruel, Milagro; Alonso i Alemany, Laura
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Ponencia presentada en el 16º Simposio Argentino de Inteligencia Artificial. 44 Jornadas Argentinas de Informática. Rosario, Argentina, del 31 de agosto al 4 de septiembre de 2015.
Fil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Active learning provides promising methods to optimize the cost of manually annotating a dataset. However, practitioners in many areas do not massively resort to such methods because they present technical difficulties and do not provide a guarantee of good performance, especially in skewed distributions with scarcely populated minority classes and an undefined, catch-all majority class, which are very common in human-related phenomena like natural language. In this paper we present a comparison of the simplest active learning technique, pool-based uncertainty sampling, and its opposite, which we call reversed uncertainty sampling. We show that both obtain results comparable to the random, arguing for a more insightful approach to active learning.
http://44jaiio.sadio.org.ar/asai
Fil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Ciencias de la Computación - Fuente
- ISSN: 2451-7585
- Materia
-
Natural language processing
Active learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- Repositorio
.jpg)
- Institución
- Universidad Nacional de Córdoba
- OAI Identificador
- oai:rdu.unc.edu.ar:11086/22140
Ver los metadatos del registro completo
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Reversing uncertainty sampling to improve active learning schemesCardellino, Cristian AdriánTeruel, MilagroAlonso i Alemany, LauraNatural language processingActive learningPonencia presentada en el 16º Simposio Argentino de Inteligencia Artificial. 44 Jornadas Argentinas de Informática. Rosario, Argentina, del 31 de agosto al 4 de septiembre de 2015.Fil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Active learning provides promising methods to optimize the cost of manually annotating a dataset. However, practitioners in many areas do not massively resort to such methods because they present technical difficulties and do not provide a guarantee of good performance, especially in skewed distributions with scarcely populated minority classes and an undefined, catch-all majority class, which are very common in human-related phenomena like natural language. In this paper we present a comparison of the simplest active learning technique, pool-based uncertainty sampling, and its opposite, which we call reversed uncertainty sampling. We show that both obtain results comparable to the random, arguing for a more insightful approach to active learning.http://44jaiio.sadio.org.ar/asaiFil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Ciencias de la Computación2015info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/11086/22140ISSN: 2451-7585reponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNCenginfo:eu-repo/semantics/openAccess2025-10-23T11:17:56Zoai:rdu.unc.edu.ar:11086/22140Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-10-23 11:17:56.679Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse |
| dc.title.none.fl_str_mv |
Reversing uncertainty sampling to improve active learning schemes |
| title |
Reversing uncertainty sampling to improve active learning schemes |
| spellingShingle |
Reversing uncertainty sampling to improve active learning schemes Cardellino, Cristian Adrián Natural language processing Active learning |
| title_short |
Reversing uncertainty sampling to improve active learning schemes |
| title_full |
Reversing uncertainty sampling to improve active learning schemes |
| title_fullStr |
Reversing uncertainty sampling to improve active learning schemes |
| title_full_unstemmed |
Reversing uncertainty sampling to improve active learning schemes |
| title_sort |
Reversing uncertainty sampling to improve active learning schemes |
| dc.creator.none.fl_str_mv |
Cardellino, Cristian Adrián Teruel, Milagro Alonso i Alemany, Laura |
| author |
Cardellino, Cristian Adrián |
| author_facet |
Cardellino, Cristian Adrián Teruel, Milagro Alonso i Alemany, Laura |
| author_role |
author |
| author2 |
Teruel, Milagro Alonso i Alemany, Laura |
| author2_role |
author author |
| dc.subject.none.fl_str_mv |
Natural language processing Active learning |
| topic |
Natural language processing Active learning |
| dc.description.none.fl_txt_mv |
Ponencia presentada en el 16º Simposio Argentino de Inteligencia Artificial. 44 Jornadas Argentinas de Informática. Rosario, Argentina, del 31 de agosto al 4 de septiembre de 2015. Fil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Active learning provides promising methods to optimize the cost of manually annotating a dataset. However, practitioners in many areas do not massively resort to such methods because they present technical difficulties and do not provide a guarantee of good performance, especially in skewed distributions with scarcely populated minority classes and an undefined, catch-all majority class, which are very common in human-related phenomena like natural language. In this paper we present a comparison of the simplest active learning technique, pool-based uncertainty sampling, and its opposite, which we call reversed uncertainty sampling. We show that both obtain results comparable to the random, arguing for a more insightful approach to active learning. http://44jaiio.sadio.org.ar/asai Fil: Cardellino, Cristian Adrián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fil: Teruel, Milagro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fil: Alonso i Alemany, Laura. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Ciencias de la Computación |
| description |
Ponencia presentada en el 16º Simposio Argentino de Inteligencia Artificial. 44 Jornadas Argentinas de Informática. Rosario, Argentina, del 31 de agosto al 4 de septiembre de 2015. |
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2015 |
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2015 |
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eng |
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