Annotation tools became an essential tool for any kind of Machine and Deep Learning processing, as well as the development of Artificial Intelligence in general. Data annotating is an integral part of data processing and model training, and it must be handled properly if you want a trustworthy and highly valuable data outcome. Every part of this processing is equally important and requires an equally precise approach. Any kind of data disturbance can result in crashing the project, with a huge, irreversible time and money wasted. Finding the right annotation tool for you/your company’s needs became a demanding task that requires detailed research, because numerous tools with different performances are available on the market, and finding the one that fits best can be a time-consuming nerve-wracking process. But if you choose the right one, it pays off multiple times. Most of them are created and built up to train various types of data inputs and corpora testing.
Manual annotation can be defined as the task of reading a pre-selected document and adding additional information about it in a form of annotation. It can be done on any form of a document, and be related to a word, phrase, or sentence in it.
It. Can be useful in order to emphasize the sections of the publication (for example, methods and history of subject development). Different forms of annotations can be seen in different types of documents and in different forms. It can vary from a short text form which is unstructured data (for example comment) supported by the Hypothesis tool way up to structured annotations with highlighted text ranges and relations between them. Structured data annotations contain multiple advantages such as stable comparison between various annotators and their work, computing statistics feature included, and other features required for Machine Learning model training purposes.