Vatsal Ghiya, CEO and co-founder of Shaip in this guest feature has talked about the key role of text annotation and why every industry is looking forward to using these tools and technology in developing ML models.
The Key Takeaway from Article is-
- In simple words, text annotation is all about specific documents, digital files, and even with associated content. Once these resources are tagged and labeled they became understandable and can be deployed by machine learning algorithms to train the model for perfection. Also, text annotation must not be to be confused with text data collection as latter simply a process to clutter and declutter datasets.
- Chatbots, voice assistants, and machine translators are steadily coming of age and with higher competition, organisations are looking to deploy text datasets to make it more accurate and responsive and proactive.
- The top 5 most impactful text annotation technology that are required for developing machine learning model are- entity annotation, text classification, entity linking, sentiment annotation and linguistic annotation. To make machine learning development a success, organisations must have the right skill and resource to analyse and labelling the datasets.
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