Like humans, machines also need to learn, understand and analyze things to produce desirable outcomes. One of the most efficient ways to make machines learn is using text annotation services. With advancements in time and technology, machines have leveled up their ability to understand human language. 

Therefore, the text annotation technique is used widely to train machines and help them communicate with humans efficiently. High-quality datasets created by annotators using the text annotation process have given a big push to the machine learning and AI models.

In this blog post, we will learn everything about text annotation and its various types.

What Is Text Annotation & How Is It Used in AI Training?

Text annotation is labeling the text, phrases, and sentences using additional metadata to make the machines learn about objects and things. Depending upon the project requirements and complexity, data sets are created by labeling the important parts of a speech, syntax, sentence, etc. After the required text is annotated, the datasets are used in AI training to make machines learn the diversity of the human language to communicate with humans effectively.

To provide efficient training to the machines you need high-quality data sets as poorly annotated text can make your machines dumb and less responsive. Therefore, it is wise to let professionals annotate the text as it requires experience and expertise. To annotate text professionally and achieve high-quality datasets, outsource the work to text annotation services providers. 

Types of Text Annotation Techniques

Large annotated text datasets are required to train NLP algorithms depending on the project requirements. Therefore, human annotators use various types of text annotation machine learning to create datasets for AI training. In this section, we will discuss each of them.

1. Sentiment Annotation

Machines can not understand emotions and sentiments like humans can. But at times,  humans also find it hard to understand the sentiments behind a phrase or a conversation. Therefore, sentiment annotation is used to train the machines and help them understand texts that have sentiments. Sentiment annotation is a type in which sentiments, opinions, and emotions hidden within the text are labeled. At first, the annotators analyze the required text to understand the sentiments and later select the best label for them to make the machines understand the emotions easily.  

A real-time example of sentiment annotation can be analyzing and labeling the customer feedback to help the machines understand the intent behind them and respond accordingly. Machines trained using accurate data sets can become part of the sentiment analysis model to track correct public opinion about a product or a service.

2. Entity Annotation

Entity annotation is used to generate training datasets for the machines by analyzing, locating, and tagging multiple entities present inside the text. Using entity annotation, the annotators can make the machines learn to identify entities in different parts of the text and the speech. Annotators go through the text thoroughly and gather all the entities in the text. After that annotators highlight the entities and provide a suitable tag for them to create the required datasets.

There are three types of entity annotation, which are provided below:

• Keyphrase Tagging – In this type of entity annotation, annotators analyze, locate and label the keywords in the given text.
• Named Entity Recognition – NER is another type of entity annotation in which annotators first locate the names of people, objects, and places in the text and then label them accordingly.
• Parts Of Speech Annotation – In this type of annotation, the annotators locate various parts of the speech in a given phrase including, adjectives, nouns, punctuations, verbs, prepositions, etc.

3. Intent Annotation

Intent annotation is one of the most important types of text annotation techniques used to create high-quality datasets for machine learning and AI-based training. Using intent annotation, the annotators create datasets that help the machines determine the intention of the users behind creating the text. The text can be created as a command, request, or confirmation, and intent annotation helps machines differentiate the different categories of the text. For eg: While communicating with automated chatbots, customers write sentences in different sentences. Customers can either request, confirm, or give a command to the chatbots. Therefore datasets created by intent annotation help machines understand the nature and intent of different types of conversations.

4. Text Classification

Text classification is also called text categorization or document classification. With text classification, the annotators read the sentences, phrases, and paragraphs and understand the intentions and sentiments for which they were created. After the annotators determine the intentions and sentiments behind the text, they classify the text into different predefined categories depending upon the type. It is quite similar to categorizing different types of products in an eStore. Text classification may sound a lot similar to Entity annotation, but it is different. In Entity annotation, annotators provide different labels to individual sentences or phrases, while in text classification an entire paragraph or sentence is annotated using a single label.

5. Linguistic Annotation

Linguistic annotation, popularly known as corpus annotation, is used for labeling the language data present within the text or the audio recordings. While using linguistic annotation, annotators identify phonetic, grammatical, and semantic elements in the text or audio data and label them to create the required datasets to train the machines. 

Usually, there are four types of linguistic annotation, which are as followed:

• Phonetic annotation: In this type of annotation, the annotator label pauses, stress, and intonation that are part of the speech.
• Part-of-speech (POS) tagging: In POS, the experts annotate different function words that are present inside the text.
• Semantic annotation: In semantic annotation, the professionals annotate word definitions.
• Discourse annotation: In discourse annotation, the experts link anaphors and cataphors to their antecedent or postcedent subjects and create the required datasets.

Using linguistic annotation, annotators create datasets for various AI training modules including search engines, chatbots, virtual machines, etc. Such datasets help the machine learning modules to understand the language data and generate correct responses.

​​How To Annotate Text?

You can annotate the text by taking help from professional human annotators that know how to label text data. Human annotators hold expertise in analyzing and tagging different parts of the text like sentiments, intentions, and others. Nowadays, human annotators have started using automated tools to speed up the text annotation process and create the required data sets quickly. The automated tools help the annotators automatically label different parts of the speech or the phrase. Annotators can then view the labeled data and accept or edit the suggestions as required.

Conclusion

In this blog post, we discussed how text annotation is used to train machines and what are the different types of text annotation used to create high-quality data sets. If you do not have the correct tools to annotate the required text, you will not be able to achieve the desired results. Therefore it is recommended to ask professionals to help you by providing text annotation services. Text annotation experts like SunTec.AI can help you achieve high-quality datasets to train your machine learning models. We use all types of text annotations from Sentiment to Relationship annotation to annotate the required text and provide you out-of-the-box experience. To know more about us you can visit our website www.suntec.ai today.

FAQs

1. How can you effectively annotate text?

There are numerous tips and tricks to follow to annotate your text effectively. A few of these tips are listed below:

1. First, analyze the text thoroughly and then try to summarize the text in your own words using bullets.
2. Now, you can highlight the important phrases and the key concepts in your text.
3. To annotate, start writing questions and comments in the margins.
4. To keep the datasets crisp, label the text using symbols and abbreviations.
5. After you have completed annotating the text, re-check if you have left any important phrase or sentence that needs to be annotated.

2. What is the purpose behind annotating text?

The main motive to annotate text is to make learning easy for the machines. Annotating text favors the ability of the machines to read, understand and learn things quickly. It also makes machines understand human language and communicate with them effectively. With text annotation, there are fewer chances that machines will make mistakes in providing resolutions to the customers and answering their queries. 

3. What are the benefits of text annotation?

Text annotation has an array of benefits in various sectors and a few of them are listed below:

• Helps in gathering the idea behind creating the text.
• Helps in elaborating the hidden thoughts in the text which favors deep understanding and quick learning.
• Helps the readers in analyzing and interpreting the text without putting effort.
• Helps the readers to make conclusions about the text.

4. Can we automate text annotation?

Yes, the tools that annotators use for text annotation support automation. With automation functionality, the text annotation tools label the required text automatically using artificial intelligence. After the text is automatically labeled, the annotators can either confirm or edit the label suggestions. Auto labeling saves time for annotators and using automated tools, they can perform text annotation quickly.

5. What resources do you need for text annotation?

For annotating text, you need a team of professional annotators as the primary resource. Annotators are required to process your data, create datasets and build models as per the requirement. Besides the experts, you will require annotation tools that will help the annotators to perform efficient text annotation.