Artificial Intelligence and Machine Learning are now part of everyday life. The consequences of these new technologies have affected how we see and interact with the world. AI and ML applications have limitless potential to radically change and drive the global economy forward. These algorithms are opening new frontiers in medicine, the arts, and finance. Natural Language Processing is at the front of all these.
Recent breakthroughs in NLP mean that people with speech impairments can conveniently communicate using automatic voice recognition software. However, to realize this technology data must be carefully annotated to train the AI models. Otherwise, all the hype around AI and ML would be wishful thinking.
To adequately train an NLP model, massive amounts of annotated texts are necessary. Below is a breakdown of the different types of text annotation tools for computer vision NLP.
Entity Text Annotation
Crucial to training chatbots, entity annotation is the foundation block for training logical NLP solutions. Recognizing, fragmenting, and annotating values is referred to as text mining.
Entity Linking
This refers to connecting similar entities to larger data repositories. This process is crucial to creating a logical NLP computer vision model.
Sentiment Annotation
Sarcasm is one of our natural reactions as humans. When giving reviews, we sometimes opt to be sarcastic whenever confronted with a bad experience at a spa or a hotel. A poorly trained software might understand sarcasm as genuine praise when it’s the complete opposite.
To avoid this, sentiment annotation/analysis is crucial. Judging from a person’s emotion or tone, people are able to label each sentiment as either positive, negative, or neutral.
Linguistic Annotation
This is also referred to as corpus annotation. This refers to tagging language data in both text and audio recordings. Labelers are tasked with identifying and highlighting phonetic, grammatical, and semantic features in both audio and text.
Intent Annotation
Intent annotation is mainly employed to decipher a user’s intention. Different users have different intentions when interacting with chatbots. Some users wish to learn about their overhead charges, others want statements, etc. This annotation technique uses different labels to categorize a user’s intent.
Conclusion
With this blog, we hope you have a better understanding of text annotation and how it is employed for computer vision. Text annotation can prove to be overwhelming when faced with ever-increasing data volumes.
To cure this, it’s always wise to outsource this non-core yet important part of AI/ML model development.
At Impact Outsourcing, we provide a professionally managed workforce backed by years of experience in data annotation. Contact us today and let us power your next NLP computer vision model.