An Annotation tool for Natural language processing tasks
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Date
2024
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Abstract
This dissertation creates an annotation tool used in Natural Language Processing
(NLP) tasks. NLP has the appropriate techniques to exploit valuable
information, provided that a large amount of annotated textual data is available.
Most annotation processes rely on manually handling a large body of
text for development and evaluation. Creating a large annotated corpus is
tedious and requires adequate computational support. Although many annotation
tools are available, their primary weaknesses lie in their specific purposes
and commercial licenses. Since the quality of the data used to train the NLP
model directly affects the quality of the results, ensuring quality control of the
annotations is essential. To facilitate this process, we have created a tool that
helps us to collect comments from social media platforms such as YouTube and
Reddit and annotate them.