Various industries of today are trying their hands on new-age fields such as Machine Learning (ML), Artificial Intelligence (AI), and Data Labeling. ML models and Natural Language Processing (NLP), also known as the sub-type of ML, offer innumerable benefits to various sectors. They analyze the text data and act as a catalyst in responding to customers through chatbots. They also recognize human emotions, thanks to sentiment analysis – one of the techniques of text annotation.
As we all know, data labeling or annotation can be classified into various types – text, images, and videos, to name a few. We will set aside image and video annotation for now and focus more on text annotation in this blog.
Text annotation
Data labeling is the engine or heart that makes supervised ML models work effectively. Text annotation is a sub-type of data labeling or data annotation. Here, the annotation process aims only at text data like DOC, PDF, ODT, etc. Though text annotation involves a lot of manual effort, the success of many speech-related applications belongs to precisely annotated text data. So, in simple terms, text annotation is crucial to any application that uses speech.
How do data scientists process text annotation can be a big question? First, they identify the labels or tags and then share the text-specific information to the Natural Language Processing model that is being trained. This is much similar to how children learn a language. Here, parents determine the labels, and the children then start learning the meaning of the words. They then distinguish the allusion, metaphor, and emotion in the sentence.
Significance of text annotation
Text annotation finds paramount importance when developing an NLP as it impacts the quality of the annotated text. Many industries are leaning towards digitized and fast customer services to stay distinguished and be noted. In addition to this, the recent pandemic has further increased the demand. Many companies have started using chatbots to deliver a hassle-free experience to customers. And, none of the companies want to disappoint their customers with an NLP algorithm that is not trained right.
Process of annotating text
In order to apply the latest text annotation techniques, companies need to equip themselves with software that makes text annotation quick and easy. There are 2 different types of tools in the market – open-source and closed-source annotation tools – that can simplify the task. Open-source tools are available free of cost and are open to anyone. And the best part is you also get to customize the tool as per the needs and requirements of your business – this comes as a bonus. In contrast, closed-source tools have a dedicated team that can help you with setting up and using the software. But, to avail of this service, you need to pay a certain amount as charges.
Alternatively, you can start developing your own tool if the tools that are available in the market do not fit your requirements. The main benefit of using in-house tools is that they are good at providing next-level data security. But, this is a slow process and requires a lot of manual effort, time, and, most importantly, a considerable sum of money. You can also choose to outsource your text data annotation tasks to expert consultants who are well-versed in using both open-source and closed-source text annotation tools.
Conclusion
As discussed, text annotation is a fundamental process in NLP development. So, it is important to annotate with care and precision. If you think you would want the help of consultants to annotate text data, you can prefer to outsource. Outsourcing agencies like Springbord offer various benefits and help steer your company to profitability. Get in touch with us today to know how our team of experts can help achieve desired outcomes.
