What Are The Different Techniques For Text Annotation?

 Text annotation, an important aspect of data annotations, helps the experts train the Artificial Intelligence (AI) model for coherency. Text annotation helps in labeling and identifying sentences to describe their features. Depending upon the scope of a project, it could include emphasizing grammar, syntax, keywords, phrases, emotions, and more. Once the AI data is trained according to the same, it is then sent to Machine Learning (ML) models. These models then learn various parts of the language e, in order to better interpret human interactions in terms of sentence construction, syntax, and tones. As the ML gathers experience using the correctly labeled data, they get better at imitating human speech (current virtual assistants).

Different Techniques For Text Annotation

Generally, companies opt for professional text annotation services to avoid mistakes such as when a text is tagged incorrectly, the AI/ML model responds in an irrelevant, inaccurate, or deceptive way. In this blog, we’ll understand the importance of text annotation and learn how to use several text annotation techniques to attain accuracy.

    Why is Text Annotation Important?  

    The major focus of text annotation is to train ML models to identify the meaning of texts. However, understanding the text is more challenging for the machine. For instance, a customer commented, “You nailed it!” on your post. As humans, we understand that they are showing excitement, and encouragement. But the natural language process (NLP)  is probably only going to pick up on the word's surface-level meaning, not its actual meaning. In particular, it could relate the term "nail" to hammer nails. 

    The multiple meanings to a sentence or human languages provide a plausible reason for the need of text annotation in NLP. Machines still have a lot to learn about context and deeper meaning, regardless of how brilliant they are. Text annotation is important because it ensures that the intended reader—in this example, the machine learning (ML) model—can understand and derive conclusions from the information presented.

    The most well-known applications of natural language processing are chatbots. There are countless instances of bot failure. The Bot generally misinterprets a customer’s inquiry and their feelings. Poorly trained chatbots are harmful to a business's reputation, user experience, and ultimately - customer loyalty. This is especially true  for those who handle customer care.

    Text annotation can help the chatbot understand the meaning, intent, and sentiment behind a comment. This helps them to improve the bot’s performance which indirectly leads to customer satisfaction with the service

    Another important use of text annotation is training the textual data extracted from scanned documents or pictures. Industries use OCR (optical character recognition) to extract this data. After extraction, the textual data is trained and fed to the models using text annotation. These OCR solutions help businesses to increase users' access to vital information.

    What Are The Different Techniques For Text Annotation?

    Here are six primary text annotation techniques that you can use to train your ML model:

    1. Entity Annotation

    Entity annotation is one of the most important techniques of text annotation. It helps in the generation of chatbot training datasets and other NLP training data. It by definition means locating, extracting, and tagging entities in the text. It labels unstructured sentences with important information. The three different types of entity annotation are:

    ● Named entity recognition (NER): NER is ideally referred to as finding and categorizing named entities within a piece of text data. This entails identifying the entities inside a paragraph (such as a person, organization, date, place, and time), and then further categorizing them in accordance with the situation. Excellent examples of NLP applications that employ NER to comprehend textual data are Grammarly, Siri, and Google Translate.

    ● Part-of-speech tagging: The process of classifying words in a phrase as nouns, verbs, adjectives, adverbs, and other descriptors is known as part-of-speech tagging. This feature is used to mark functional speech components in the text data.

    ● Keyphrase tagging: This method is used to find keyphrases or keywords inside text. This is frequently used to enhance search-related features for databases, e-commerce platforms, self-serve assistance areas of websites, and other similar applications. It is also known as keyword extraction.

    Entity annotation teaches NLP models how to recognize named entities, keyphrases, and bits of speech inside a text. Annotators must carefully read the text, identify the target entities, highlight them on the annotation platform, and select a label from a specified list. Moreover, entity linking is frequently used in conjunction with entity annotation to aid NLP models in learning more about named entities.

    2. Entity Linking

    After marking the named entity, the annotator moves towards linking those entities to the larger data set. This process is known as entity linking. For instance, linking entities to Wikipedia. The annotator analyzes the named entity and then links it with knowledge databases about them. Entity linking enhances both the user experience and the search functionality. The entire process helps to add extra details about the entity.

    3. Text Classification

    Text classification assigns a specific label to documents or groups of phrases. It helps to annotate the entire line of the text under a single label. With the help of text annotation, a sizable amount of texts or documents may be divided into the proper categories, such as document classification, product categorization, intent annotation, and sentiment annotation. However, text classification can be as easy as placing a section of the article in the entertainment or sports category, or it could be as difficult as classifying items in an online store. 

    ● Document Classification: This categorizes and organizes documents that contain text-based material.  

    ● Product categorization: This classifies goods and services into understandable divisions and groups to enhance user experience and search relevancy. 

    ● Sentiment Annotation: This helps to categorize text according to the feeling, opinion, or emotion expressed in it.

    ●Intent annotation: This examines the need or desire behind a text to categorize it according to the intent such as a request, command, or confirmation.

    4. Sentiment Annotation

    Annotating sentences with their matching sentiments are known as sentiment annotation. It helps in labeling emotions and opinions of the text. However, annotating the sentiment in the text can be difficult. Even a human annotator can make mistakes in understanding the real sentiment behind the text. Texts that contain humor, sarcasm, or other informal ways of communication, make it considerably more difficult for a machine to identify concealed implications within. However, accurate sentiment annotation is necessary in the case of complicated phrases, particularly, for use cases that are not generic and have a particular sentiment for a type of content. 

    Brands use Sentiment Annotation to evaluate social media posts and comments. This helps them to understand how the public perceives them and which platforms tend to have a more positive or negative sentiment. In return, brands can change their communication tactics with the help of this information.

    5. Intent Annotation

    Intent annotations are generally used for virtual assistants and chatbots. It helps to examine the need or desire behind a text to further categorize them according to requests, commands, confirmations, disappointments, and other specific intents. This helps the chatbots to understand the intent of the user and then respond accordingly. For instance, a customer drops a text, “The product received is damaged.” Now, the chatbot, using text annotation, detects the intent of the text (here, disappointment), processes the message, and then delivers the response. The requirement for intent annotation emerges since it might be frustrating for the user if the chatbot is unable to respond with the appropriate message.

    6. Linguistic Annotation

    Linguistic annotation included every other technique of text annotation. Only the main distinction here is that the annotation process is carried out on linguistic data. This involves identifying and highlighting grammatical, semantic, or phonetic aspects in the text or audio data. 

    ● Phonetic Annotation: This helps to annotate tone, stress, and pause. These are generally used by chatbots, search engines, and virtual assistants to understand what the customer wants to say.

    ● Semantic Annotation: This helps to label the metadata of the original text. For instance, users search for “top 5 OTT platforms.” The search engine will display Netflix, Amazon, etc. Semantic annotations help label the meaning of such jargon.

    ● Discourse Annotation: It includes connecting anaphors and cataphors to their antecedent or descendent subjects. For instance, Jamie likes apples. She eats it every day. Discourse annotation helps to refer Jamie to as ‘She’.


    Overall, text annotation is essential for modern technologies like chatbots to work properly. Using these six techniques, businesses can improve their performance, sales, and customer satisfaction parameters. However, there are different methods for text annotation from hiring in-house experts to freelancing, outsourcing, etc. But one of the best annotation methods is combining annotating tools and human annotators. Human annotators, using the appropriate tools, yield better outcomes. They can better comprehend complicated emotions and skillfully annotate extremely technical topics. As a business owner, you can either hire an expert and the tools needed or directly outsource text annotation services. Both the ways will ease your annotation process and produce the desired results.

    Author Bio:

    Jessica is a Content Strategist, currently engaged at Data-Entry-India.com- a globally renowned data entry and management company -for over five years. She spends most of her time reading and writing about transformative data solutions, helping businesses to tap into their data assets and make the most out of them. So far, she has written over 2000 articles on various data functions, including data entry, data processing, data management, data hygiene, and other related topics. Besides this, she also writes about eCommerce data solutions, helping businesses uncover rich insights and stay afloat amidst the transforming market landscapes.

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