Comparative Study of Annotation Tools and Techniques

Description

– Comparative Study of Annotation Tools and Techniques –

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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.

Introduction

In recent years, big data has become a salient research topic in the IT industry. A huge volume of data is generated on daily basis, owing to the fact that numerous numbers of devices are connected to the internet.

The huge volume of data come from different sources, such as the World Wide Web, Ecommerce, and social media platforms like Facebook, twitter etc.

Big data can be of two types, structured and unstructured data; structured data are the usual data that can be stored in a relational database, the query of these data is easy and analysis on them can be seamlessly done (Kiefer, 2016).

The unstructured data, are usually data from email, pictures, document, video files, audio and other sources. It is difficult to process these unstructured data with relational databases, hence there is the challenge of managing such data.

In this research the technique of annotation will be studied; various annotation tools and techniques have been proposed, that help in the analysis of big data.

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