Most of the data we deal with today is unstructured. And when we use this unstructured, unlabelled data to train an ML model, the model needs to work hard to correctly identify the text, image, or video.
But when you train an ML model via supervised learning with labeled datasets, you give a head start to train faster, to understand and decipher the information in a better manner.
So both data annotation and data labeling are necessary for drastically cutting down the time taken for ML models to reach a good level of accuracy.
A lot of us use the terms text annotation and text labeling interchangeably. While the basic idea of both remains the same — to train ML models — there are subtle differences in the approach and purpose.
This blog will take you through the fundamentals of text annotation and text labeling and the differences between both.
What Is Text Annotation?
When building an AI/ML model, you need to train the algorithm to classify, understand and give a relevant output. The first place to start this process is data annotation.
Text annotation is a type of data annotation technique used to train algorithms to understand what the text is — similar to the way humans perceive it.
Most often, through text annotation,
your ML models learn to understand the human emotions behind the data (text), identify and correlate patterns and make connections between multiple chunks of text.
Data scientists add metadata to the input to describe what a piece of text means.
So when a machine learning model receives a text input based on the metadata and the parameters assigned, it can then classify the data based on attributes and make as much of a close connection as possible with the human intention behind it.
What Is Text Labeling?
Simply put, data labeling is the process of tagging what a piece of input is. There are several techniques in data labeling for audio, video, graphics, and text.
Here are a few good examples of data labeling:
- Identifying the item in an image,
- The movement in a video and,
- The meaning behind a piece of text or audio.
Machine learning engineers use text labeling to train machine learning algorithms to quickly gauge the meaning behind the input and proper an appropriate output.
With text labeling, the engineers can train the machine to differentiate between different tags of text and make accurate predictions.
Your ML models then add a tag or a label to a piece of input based on its training. This label will make the information more meaningful to the model.
Comparison Between Text Annotation and Text Labeling
1 - What Are the Intents Behind Text Annotation and Text Labeling?
One of the critical differences between text annotation
and text labeling
is the intent behind them.
trains the machine to see the world in the way humans see it.
Here the machine goes through various processes to properly understand the emotion behind the data (text), the intent of the person typing the text, reasoning, and goals.
On the other hand, text labeling
teaches your models to identify the inputs with the help of tags.
minimizes manual intervention by training supervised machine learning models to distinguish and recognize information by tagging the data and grabbing information from the metadata.
2 - How Are Text Annotation and Text Labeling Used?
is often the fundamental step in creating training datasets for any algorithm.
The annotated data is used to train the models to view the input, similar to the way humans see it.
is more complex.
Data labeling is commonly used in NLP algorithms to identify various features and characteristics of the datasets.
Data scientists and engineers also use data labeling in advanced algorithms—especially where there’s a massive load of information for the ML models to process. Here text labeling is used to shorten the training time.
3 - How Do Text Annotation and Text Labeling Work?
In most cases, data scientists use annotation
for visualizing the input in various visual perception models.
Text annotation, in particular, helps to picture an image of what the text means through computer vision and to highlight the critical intent behind it.
Many predictive and preventive algorithms require text labeling
to track, identify and make accurate assumptions about the future.
is preferable for visual analysis to create a computer vision of the input, while labeling
is for complex NLP models.
Train Your Models with Text Annotation and Text Labeling
If you’re confused about choosing annotation
then look deep into your use cases and applications of ML models.
You can also consult data annotation and labeling specialists to guide you through the process.
Reach out to our data experts at Traindata
, having more than 15 years of experience in labeling and annotation techniques.
Our team has experience training algorithms with human-in-the-loop annotation and various other labeling and data annotation techniques.
Please email us at [email protected]
to discuss your data needs.