Data in the form of text is crucial for organizations because it aids them in analyzing and making better business decisions. The analysis process can be sped up by annotating this textual data. According to reports, around 163 zettabytes of data available online will be unstructured by the end of 2025. This means that around 80% of the online data will remain unstructured. Hence, text annotation is the need of the hour.
With the help of text annotation, a lot of unstructured data can be accurately labeled and classified. Through this process, businesses can successfully automate their services. For example, smart chatbots are being introduced on consumer websites like Flipkart, Myntra, and more where your text queries are acknowledged with automated responses. Even on the website of Expert Data Annotation, a smart chatbot pops up in the bottom right corner.
What is Text Annotation
In the process of text annotation, footnotes and comments are added to the text so that some main points can be highlighted and classified into parts. It enables us to summarize the entire text and also to flag the important points in large textual data. This eases out the reading process for a reader an helps them in digesting complex information easily.
Once the important parts are highlighted, they are labeled to train the machine learning model. Text annotation is done to understand the grammatical structure of the textual data along with the keywords, parts of speech, emotions, feelings, and more.
Types of Text Annotation
Following are some of the commonly used text annotation types:
- Entity Annotation:
Entity annotation is the process of recognizing and annotating entities, which are words or phrases in text, such as specific names or keywords. Training natural language processing models—which are used to create chatbots and virtual assistants—requires entity annotation. For NLP models, the combination of entity linking with entity annotation offers an improved learning environment. We’ll talk about entity linking below.
- Entity Linking:
Entities are further linked to more expansive data sources through entity linking. Through this method, an entity is given a specific identity based on the textual data, such as a company’s name or contact details. The goal of entity linking is to enhance user experience and search results.
- Text Classification:
It is a more comprehensive method of classifying and labeling data. Labeling a line or section of text is the first step in text classification or categorization. Annotators carefully read, evaluate, and identify the primary idea and topic of each piece before further categorizing it into the assigned categories.
- Sentiment Annotation:
The goal of sentiment annotation is to teach AI models how to identify feelings, attitudes, and viewpoints in textual data. It is one of the trickiest tasks under text annotation, nevertheless. It is much harder for machines to grasp the true meaning and emotion included in a text because occasionally even humans are unable to do so. Sentiment analysis and annotation, however, can help. AI models are trained to comprehend emotions and feelings by providing them with textual input that has been annotated with sentiments.
Use Cases of Text Annotation
Following are some of the most relevant use cases of text annotation:
- Healthcare Industry:
With the help of text annotation, data can be extracted automatically from clinical trial records and medical documents. This eases the research process.
Further, the patient records are also analyzed which assists the doctor in a better diagnosis and treatment design. Patient outcomes are greatly improved with the help of text annotation.
- Insurance:
It allows a better evaluation of risk based on the textual data submitted by the customers. It also helps in a better claim processing process. Even fraudulent claims are detected quite accurately with the help of text annotation.
- Banking:
Any fraudulent money laundering pattern is identified well within the time to avoid any issues at the customers’ end. Moreover, with the help of text annotation, the workflow can be streamlined.
For each customer, the evaluation of loan rates, credit scores, and more can easily be done with the help of text annotation.
There are many more real-world applications of text annotation which would be specified in some other blog.
How Expert Data Annotation Can Help
So, these was all about text annotation. The requirement of this is increasing day by day due to the benefits it offers. A model’s ability to label and annotate text is directly proportional to the rigrousness of the training it undergoes. Generally, the models trained on quality data produce optimal results.
Those looking for quality datasets for AI and LLM training should consider Expert Data Annotation. With a commitment to excellence, Expert Data Annotation ensures the validity, relevance, and accuracy of data.
We offer you a wide range of datasets for your custom model training across a wide range of industries. We follow the best privacy and data protection practices in the entire market.
FAQs
Ans: – In the process of text annotation, footnotes and comments are added to the text so that some main points can be highlighted and classified into parts. It enables us to summarize the entire text and also to flag the important points in large textual data.
Ans: – With the help of text annotation, the important and required parts from a large volume of textual data can be highlighted and labeled. It also provides an overall structure to the data. It enables automation in an AI/ML model.
Ans: – Text annotation is used in industries like healthcare, telecom, insurance, and more.
Ans: – The accuracy and effectiveness of these models depend on the quality of data they are trained on. Thus, one must ensure that the data sets fed to a text annotation model during the training phase are of high quality.
Ans: – For sourcing high-quality datasets for training AI models look no further than Expert Data Annotation. Their data sets are curated by industry experts hence, ensuring better performance and accuracy. They also follow strong data protection practices.