Feed Your AI with the Best Text Data Annotation
Mar 31, 2022
Have you ever been astounded by how well your mobile phone predicts what you’re thinking as you type your text responses? Or, have you ever been wowed by how a phone operator who wasn’t even a human answered your questions or refunded your money? Behind every such unexpected occurrence, concepts such as Artificial Intelligence, Machine Learning, and, most importantly, Natural Language Processing (NLP) are at work (Natural Language Processing).
NLP is one of the most significant recent breakthroughs, with machines gradually evolving to understand how humans talk, emote, comprehend, respond, analyze, and even mimic human conversations and sentiment-driven behaviors.
However, getting here wasn’t easy, and the road ahead won’t be any easier. So to defy the common standards, Machine learning (ML), nowadays, is teaching machines how to learn to communicate efficiently enough in natural language after being trained on accurately annotated text data. Which allows it to carry out the more repetitive and mundane tasks humans would otherwise do. This frees up time, money, and resources in an organization to enable focus on more strategic endeavors.
To explain more, data annotation is the process of labeling data with descriptions or information in order for machines to understand it.
In terms of NLP, the data annotation technique we use is known as text annotation.
Now, recent advances in NLP have highlighted the growing demand for textual data in fields as diverse as insurance, healthcare, banking, and telecommunications which made Text annotation very important due to its role in ensuring the target reader, in this case, the machine learning (ML) model, to perceive and draw conclusions from the information provided.
Let’s dig a little deeper into this.
What is text data annotation?
The goal of natural language processing (NLP) is to develop programs that can recognize understandable human speech and communicate it back to create simple, natural interactions with artificial intelligence (AI).
The process is powered by data, as is the case with all ML projects. That data in this case is naturally formed written text (such as a product review) that must be transcribed and annotated. As a result, the ML algorithm can form associations between real and expressed meanings, allowing for more natural interaction.
Sentiment analysis (also known as opinion mining) is a method for determining whether text data has a positive, negative, or neutral connotation.
This information is derived from social media monitoring, brand monitoring, customer service analysis, customer feedback analysis, and direct market research.
This gives you an idea of what people are saying about your products and services while also training the algorithm to detect these sentiments automatically.
This technique distinguishes user intentions. Different users have different intentions when interacting with chatbots. Some request statements, while others demand responses for overcharges, and still others confirm the debit of money, among other things. This technique categorizes these various types of desires using appropriate labels.
This is the most important text annotation technique for identifying, tagging and attributing multiple entities in a given text or sentence.
It is simpler to explain by naming the various types of entity annotation:
-Named entity recognition (NER): is the process of annotating entities with proper names.
-Key tagging: is the process of locating and labeling keywords or phrases in text data.
-POS (part-of-speech) tagging: The recognition and annotation of functional elements of speech (i.e. adjectives, nouns, adverbs, verbs, etc.)
This task helps train the AI to recognize not only what is said, but also what is being discussed.
This is also known as document classification or text categorization. Annotators read chunks of paragraphs or sentences to understand the sentiments, emotions, and intentions behind them. They then divide the text into categories based on their comprehension, as specified by their projects.
Linguistic annotation incorporates elements of everything we’ve discussed thus far, with the only difference being that the annotation is performed on language data. As a result, this technique employs an additional annotation type known as phonetics annotation, which tags intonations, natural pauses, stress, and other characteristics.
So those were the various text annotation techniques. We believe you now have a better understanding of how simple NLP applications perform so accurately on our smartphones. Text data sourcing and labeling become more complex as projects become more complex. As a result, it is critical to work with data annotation experts to obtain the most precise AI training data for your modules.
You can make your data meaningful and train your algorithm with our labeling and classification solution.
We adapt to your unique setup. Enjoy 100% flexibility when it comes to data and file structure.
We offer text annotation solution in multiple languages.
Contact us today to learn more. https://ubiai.tools