PAL, a tool for pre-annotation and active learning
(2017) In Journal for Language Technology and Computational Linguistics 31(1). p.91-110- Abstract
- Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming. The aim of “PAL", a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation. The output of the pre-annotation is provided in the... (More)
- Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming. The aim of “PAL", a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation. The output of the pre-annotation is provided in the annotation format of the annotation tool BRAT, but PAL is a stand-alone package that can be adapted to other formats. (Less)
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https://lup.lub.lu.se/record/63763f56-a18c-47bb-9ede-a1f2f225778c
- author
- Skeppstedt, Maria
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal for Language Technology and Computational Linguistics
- volume
- 31
- issue
- 1
- pages
- 19 pages
- ISSN
- 2190-6858
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- 63763f56-a18c-47bb-9ede-a1f2f225778c
- date added to LUP
- 2017-05-15 20:50:16
- date last changed
- 2019-03-08 02:29:00
@article{63763f56-a18c-47bb-9ede-a1f2f225778c, abstract = {{Many natural language processing systems rely on machine learning models that are trained on large amounts of manually annotated text data. The lack of sufficient amounts of annotated data is, however, a common obstacle for such systems, since manual annotation of text is often expensive and time-consuming. The aim of “PAL", a tool for Pre-annotation and Active Learning” is to provide a ready-made package that can be used to simplify annotation and to reduce the amount of annotated data required to train a machine learning classifier. The package provides support for two techniques that have been shown to be successful in previous studies, namely active learning and pre-annotation. The output of the pre-annotation is provided in the annotation format of the annotation tool BRAT, but PAL is a stand-alone package that can be adapted to other formats.}}, author = {{Skeppstedt, Maria and Paradis, Carita and Kerren, Andreas}}, issn = {{2190-6858}}, language = {{eng}}, number = {{1}}, pages = {{91--110}}, series = {{Journal for Language Technology and Computational Linguistics}}, title = {{PAL, a tool for pre-annotation and active learning}}, volume = {{31}}, year = {{2017}}, }