The natural language processing (NLP) market has grown by $18.9 billion(2023) to 68.1 billion(2028), at a CAGR of 29.3%. This statistic shows an increase in the usage of machines in different firms. However, 2/3rd of NLP models can’t solely handle complex data issues. Thereby, text annotation will support AI and NLP models so they can easily understand human instructions. Let’s understand the importance of text annotation and how it affects algorithms.
What is Text Annotation?
People can add comments and footnotes through text annotation, to a text body. People can usually do text annotation while editing and reviewing a draft and adding comments before sending the draft to the editor. The labeled data makes sure that NLP models can recognize and interpret accordingly.
Importance of Text Annotation in NLP
Data scientists are designing software that needs a lot of understanding of complex issues of traditional text labeling procedures. This is all done to cater to the advanced needs of the user. Usually, this process is used to label text in certain documents that are difficult to label through traditional methods. Besides this, text annotation saves time and promotes operational efficiency. Text annotation works just like image classification, where software labels certain essential images.
In text annotation services, labeling means highlighting some words in a sentence and categorizing them in different categories, including negative, positive, and neutral. Annotating text includes highlighting parts of speech such as subject and noun. Labeling phrases in 100 words and summarizing a long blog thesis or article.
Types of Text Annotation
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Sentiment Annotation
Through sentiment annotation, businesses can evaluate and label opinions, user sentiments, and emotions in a specific text. Emotions can’t be easily understood by humans as well, so it’s quite difficult for machines to understand different sentiments, including sadness, happiness, anger, frustration, and humor.
For instance, “you are doing amazing” is a sentence that shows the excitement of the narrator. It’s equally important for software to understand this narration and forward it to the desired place. However, a human would be equally required to understand the exact meaning of that statement so that it can train AI models.
Authentic training data and strong sentiment analysis can boost business through:
- Product reviews
- Emails
- Social Media Posts
- Client Testimonials
- Public Opinions
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Text Classification
Recognizing and classifying different body text in a document is known as text classification, text labeling, or tagging. This is usually used to categorize text into subcategorized groups. Let’s understand subcategories under this type:
- Product Categorization
- Document Classification
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Product Categorization
Categorize products and services into subcategories that will eventually improve UX. This process is helpful in aligning e-commerce, including product listing, sorting images, and providing a department list.
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Document Classification
It helps in recalling previous documents easily and sorting and categorizing them automatically. For instance, HR departments that have massive amounts of emails and employee documents, including contracts and job offers, may find it easy to classify them.
3. Entity Annotation
This type of text annotation proves to be essential in training NLP and AI datasets and the generation of chatbots. It’s the essential process of extracting, finding, and tagging certain things in a text. Following are certain types of entity annotation:
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- Parts of Speech include annotation and discernment of the different speech elements.
- Named Entity Recognition involves annotation with proper nouns.
- Keyphrase tag labels and identify keywords in text.
Hence, entity annotation trains AI and NLP models to recognize keyphrases and parts of speech in text annotation. Annotators can now easily locate the targeted things and label them within the prescribed time. Furthermore, entity recognition can be associated by linking to trained NLP systems.
4. Intent Recognition
This type of text annotation includes specific methods that can easily determine the user intention behind a text. For example, complaints, feedback, requests, and suggestions come under this type. Moreover, intent recognition has certain input types and determines the expressions of humans through written text. For instance, in automated calls, the AI models learn from human voices and understand if the customer is looking for a representative and wants to pay bills. These text annotation types are crucial to save time and provide low-key scenarios.
Final Verdict
Text annotation plays a crucial role in training AI, ML, and NLP models. People moved towards advanced technology because traditional approaches were not able to cater to the massive needs of firms. For example, HR needed a tool that could highlight different aspects of the text in documents. Documents include employees’ contracts and their background information. So, text annotation is fulfilling its purpose of highlighting different aspects of more documents in less time.