Text Annotation
Artificial Intelligence algorithms rely on a substantial amount of annotated data.
A large volume of annotated or labeled data is necessary to develop an artificial intelligence solution. This entails having a team of individuals who specialize in the task and are trained in various annotation tools to train a model capable of understanding human natural language, known as NLP (Natural Language Processing).
Text annotation serves as the foundation for creating Artificial Intelligence solutions that emulate our natural way of communicating. A myriad of applications employs this technique to enhance interaction with their users.
Precision and Quality
When we create AI models that adapt or learn so well from the data that was used for training, we say that these models are overfitting or over-adjusted.
In practice, overfitted models may perform exceptionally well during testing. However, when deployed in production or confronted with an entirely new set of data, their performance tends to decline significantly.
It's akin to suggesting that the model "memorized" the training dataset without genuinely understanding the underlying distinctions.
Machine learning models relying on poorly annotated datasets can mislead your systems, directly impacting the intended business goals of the Artificial Intelligence solution.
The Types of Text Annotation
01. Entity Annotation
Entity annotation is one of the most crucial processes in generating datasets for applications utilizing NLP (Natural Language Processing), such as Chatbots. It involves locating, extracting, and marking entities in the text, such as proper names, places, and objects.
02. Entity-Relationship
While Entity Annotation assigns meaning to the words in the text, relationship annotation connects these entities to provide a broader meaning to the Artificial Intelligence model. It involves understanding the interdependencies contained in the text.
03. Text Classification
In text classification, labelers analyze a document, sentence, or paragraph and classify it according to subject, intention, or feelings with the aim of understanding what is being expressed.
04. Sentiment Annotation
Sentiment annotation evaluates the attitudes and emotions expressed in a text, labeling it as positive, negative, or neutral, for example.