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Text annotation is a critical process when preparing potentially strong Natural Language Processing (NLP) models. Here are a couple of real-life examples showcasing its successful implementation:
1. Fake News Detection:
• Challenge:
Current social networks cannot control fake news articles – these articles
appear on the users’ feeds.
• Solution:
Document labelling was done for NLP model training. For real and fake news, the
annotators followed certain guidelines and marked the news articles as ‘Real,’
‘Fake,’ or ‘Satire.’
• Success:
These trained models can now process large amounts of texts and can detect
false information with very high accuracy and increase the reliability of the
posted articles and news.
2. Improving Customer Service Chatbots:
• Challenge:
Chatbots experience several limitations such as difficulties in comprehending
sarcasm, multiple questions, and emotional expressions.
• Solution:
Customers’ calls were textually annotated according to their sentiment as
positive, negative, or neutral and according to their purpose as a request,
complaint, and so on.
• Success:
The NLP models trained on annotated data help the chatbots to identify the
emotional state of the customer and reply to them accordingly. This results in
more natural and friendly main as well as secondary communication with
customers, which enhances satisfaction.
3. Medical Research & Drug Discovery:
• Challenge:
However, pulling out data from huge clinical documentations and research
articles can be very tiresome.
• Solution:
The method of text annotation was applied to recognizing separate entities,
such as diseases, drugs, and genes in the medical text documents.
• Success:
Most of these NLP models are trained with annotated data to conduct efficient
analysis of medical literature. This help in quicker search, creation of new
medications and developing particular course of therapy for the patients.
These are just a few examples, and text annotation finds
applications in various fields:
• Finance:
Today’s news filtering and its use to make prediction on stock market
direction.
• E-commerce:
Product recommendations have annotations that are used for fine tuning and
making the custom user experience better.
• Social
Media Analysis: Text annotation of social media to predict the demographic and
sentiments of the targeted audience.
Due to text annotation technology, machines are given the
capability of processing natural human language, thereby contributing immensely
to revoluntionizing different industries.
