Abstract
A huge amount of data is generated on daily basis. The generated data can be both structured and unstructured data. The sources from which most of the unstructured data are found are the dailies, social networks (posts from Facebook, tweeter, etc.), event reporting (for example recounting an accident), etc. One of the biggest challenges in Big Data analysis is the use of unstructured data. There is need to structure the corpus so as to permit analysis and one of the approaches for structuring unstructured data is the technique of annotation. Annotation could be fully automatic, semi-automatic, or fully manual (human). The technique of annotation has been of important help in different domains and sectors (machine learning, education, health, commerce, etc.). For example, in machine learning especially for supervised learning where annotation is used in the training phase to label data. In this research we studied and analysed different annotation tools and techniques. The studied tools were tested and their most important features that should be taken into consideration when choosing a tool were used for the comparison.
Fortunee, M (2021). Comparative Study Of Annotation Tools And Techniques. Afribary. Retrieved from https://tracking.afribary.com/works/comparative-study-of-annotation-tools-and-techniques
Fortunee, Musabeyezu "Comparative Study Of Annotation Tools And Techniques" Afribary. Afribary, 13 Apr. 2021, https://tracking.afribary.com/works/comparative-study-of-annotation-tools-and-techniques. Accessed 24 Nov. 2024.
Fortunee, Musabeyezu . "Comparative Study Of Annotation Tools And Techniques". Afribary, Afribary, 13 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/comparative-study-of-annotation-tools-and-techniques >.
Fortunee, Musabeyezu . "Comparative Study Of Annotation Tools And Techniques" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/comparative-study-of-annotation-tools-and-techniques