Date2022-03-07 12:00

Document Annotation: Everything You Need to Know

As far as training data is concerned, document annotation is one of the key bases. It can fortunately be used in a slurry of contexts and for different types of applications

Document Annotation: Everything You Need to Know

Document Annotation and its Everyday Uses

Document annotation is one of the fundamental cornerstones of modern AI-powered technology and without it, the gap between humans and machines would be difficult to reduce. The process of document annotation is not complicated but it serves an incredibly important purpose. By using a combination of human annotators and platforms, it is possible to create better training data so that all devices and services that are dependent on machine learning and AI can benefit and become smarter. Document annotation is a time-consuming and expensive process but by automating it through the use of annotation solutions and platforms, the cost and time requirements can be reduced significantly. Let’s take a closer look at the process of document annotation, its importance, and its everyday uses:

What is Document Annotation?

Document annotation is the process of adding labels to and organizing data in such a way that it becomes possible for computer systems to extract specific data from text sources such as documents. Without document annotation, it would not be possible for search engines to quickly extract specific data from a variety of documents such as long texts, e-books, invoices, and legal documents. As digitalization becomes more widespread, document annotation becomes more important. This is especially important in the case of historical documents. Thanks to digitalization, these documents are now available in digital format and their contents can be analyzed and categorized much easier thanks to document annotation.

For example, a human reader may have no problem understanding the deeper meaning behind the phrase “you are killing it”, a machine learning model, however, may not be able to understand this sentence as easily. In fact, the sentence may be mislabelled as negative or violent when in fact, in most cases, it has a wholly positive meaning. This is where document annotation comes into play, by labeling text and providing the definition of this text to a machine learning model, computers can learn to interpret the text correctly and understand the deeper meaning in complex human speech and expressions. Correctly annotated documents can be used as training data to teach ML- and AI-models how to interpret specific text more accurately and ML- and AI-models can use the information from annotated documents as a reference point for future use.

Why is Document Annotation Required?

Document annotation makes it possible for humans and machines to better interact with each other. This might sound far-fetched at first but think about it for a second, one of the core purposes of document annotation is to make it easier for computers to understand natural language queries and respond to search queries more effectively. Document annotation greatly improves the training data that machine learning models require to power technology such as chatbots and makes it possible for machines to understand complex human language better. Data is one of the most valuable assets that a company can own but if data is not clearly structured and accessible, it is not easy to use, which reduces its value.

Annotated documents make it easier for search engines to find information in a variety of document types, including PDF documents, long texts, and other business documents like invoices and estimates. Since most businesses use a large number of documents on a daily basis, it only makes sense that these documents must be annotated so that the data in them can be found easily and quickly when needed. Aside from making it easier to find data, document annotation also plays an important part in the archiving and indexing of data. Annotated documents can be indexed much quicker and easier because the annotations that they contain make it possible for machine learning algorithms to analyze the contents of the data and automatically index the document correctly according to specific parameters such as document type, contents, and sensitivity.

Training data is used to teach machine learning algorithms to automatically index data. Properly annotated documents are an essential part of this process because it teaches AI systems to correctly index documents and the information that they contain.

What are the Different Types of Document Annotation

One size does not fit all when it comes to document annotation. Different types of documents can be annotated in different methods, depending on what the data will be used for and what the desired result is. Some of the most frequently used annotation methods include:

Named Entity Recognition (NER)

This form of document annotation is also referred to as named entity recognition and it refers to the process of adding labels to predefined words or phrases. This type of annotation works well when the desired end result is to make it easier for machines to understand the subject matter of a specific text.

Named entity recognition has a large range of real-world applications, including:

  • Customer Service Applications: Chatbots and some other automated processes can benefit from named entity recognition. For example, customer service requests can be routed to specific departments or people based on the contents of an email or instant chat message. By recognizing or annotating specific words in training data and teaching AI-powered systems to look for these phrases and take specific actions when they are found, customer service systems can be further automated and improved.

  • Hiring and Recruitment: Named entity recognition can be used to look out for specific words or phrases in employee CVs or applications. By using automation, AI-powered systems, and machine learning, the work of HR departments can be significantly reduced. For example, named entity recognition can be used to train machine learning models to scan through vast numbers of job applications and find the right candidates. A summary of the best candidates can then be presented to human employees for review and selection.

  • Medical Industry: In the healthcare sector named entity recognition can be used to process a variety of important information. By using named entity recognition documents like patient records, medical reports, and medical research can be quickly analyzed to find the appropriate information.

As can be seen from these examples, named entity recognition is a very versatile form of document annotation that can be used in almost any industry.

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Sentiment Annotation

It can be difficult at times for humans to understand the sentiment behind a specific phrase or sentence, let alone for machines. This is where sentiment annotation becomes important. Sentiment annotation is aimed at helping machine learning algorithms to understand the meaning or sentiment behind a specific phrase. By using sentiment annotation, machine learning algorithms can decide whether a phrase or word is positive, negative, or neutral. Understanding the sentiment behind the text is quite important and can be used in a variety of ways, including:

  • Digital Marketing and Social Media: Sentiment annotation can be used to analyze social media posts to better understand public opinion. This is especially useful for companies that rely on social media marketing and by teaching an AI model to identify the sentiment of the text that makes up a specific social media post, companies can gain a better insight into consumer opinions. This data can then be used to develop different communication strategies.

  • Deeper Customer Insights: Sentiment annotation allows AI models to better understand the sentiment behind customer interactions like reviews, e-mails, and instant messages. By analyzing these messages and looking at the sentiment behind a specific message, AI systems can automatically direct queries to specific departments or employees

  • HR & Employee Engagement: Similarly to customer feedback and interactions, sentiment annotation can be used to train AI models to better interpret employee feedback and determine the sentiment behind a specific message or interaction. This type of document annotation is especially useful when a large volume of responses needs to be analyzed in a short period of time. One example of this is employee satisfaction questionnaires. By using sentiment annotation, employee responses can be analyzed much quicker and more accurately.

Semantic Annotation

Semantic annotation is mainly used to make it easier for virtual assistants and chatbots to better understand customer queries. This form of document annotation adds industry-specific jargon to phrases so that chatbots can recognize jargon that a customer may use and respond appropriately to the query.

How is Document Annotation Done?

Document annotation can be done in a variety of ways but the most convenient way to do document annotation is by using an automated document annotation platform. Document annotation can be a costly and time-consuming process and by having an automated platform do much of the work for you, you can save both time and money.

As mentioned before, it is crucial that text is annotated correctly because the incorrectly annotated text will influence the accuracy of AI-powered systems. While using an automated annotation platform is by far the easiest and the most cost-effective document annotation solution, there are alternative methods. The appropriate method is almost always dependent on the data in question and the desired outcome. For example, data that will ultimately be used as training data requires much more detailed annotations and might need extra care to ensure that information is labeled correctly. In these cases, it might be prudent to use a human annotator.

It is also important to remember that there are various types of document annotation and that each type is approached a bit differently. Some examples of how to document annotation are done include:

Named Entity Recognition (NER)

The first step with NER annotation is to determine the categories that will be used in the annotation process. For example, cities, countries, provinces, occupations, and so on. To start with an annotator would look at a specific block of text and categorize parts of the sentence into one of the predetermined categories.

Sentiment Annotation

In this type of annotation, the annotator would look at a specific block of text, sentence, or phrase and label it as positive, neutral, or negative. It is often prudent to use multiple annotators when very precise sentiment annotation is required. This is because sentiment can be subjective and not everyone shares the same sentiment regarding a specific phrase. What might be negative to someone could be neutral or positive to someone else. By having more than one annotator work on the same piece of text, you can ensure that the text is not annotated in a subjective manner.

Semantic Annotation

In this type of annotation, the annotator will be shown a specific piece of text and they will then add industry-specific jargon to the text. Semantic annotation can also be used to account for factors like sarcasm or irony.

As can be seen from the above, using human annotators to perform a specific annotation task can be quite expensive and time-consuming. For this reason, using an annotation platform is usually the preferred option. This is especially important when speed and budget are important considerations.

Ensuring that document annotation is done correctly

As with most things in life, it is important to ensure that annotations are done properly to get the best possible results. This can be done in a variety of ways but the most frequently used methods are:

  • Multiple Annotation: It is generally considered that texts which have been annotated more than once are of higher quality. For this reason, texts are often annotated up to three times by separate annotators, resulting in less subjective and higher quality annotations.

  • Inter-Annotator Agreements: By measuring the level of agreement between annotators, it can be determined whether or not a specific annotation is correct. If two or more annotators agree and one disagrees it can generally be considered that the two who are in agreement are correct.


Document annotation is one of the most important aspects of modern AI-powered technology because it serves to decrease the gap between humans and machines. Document annotation makes it possible for machines to develop a deeper understanding of human language and the technology has several considerable applications. High-quality document annotations are one of the best ways in which training data can be improved, which will ultimately result in better technology and allow humans to focus on more complicated tasks while automated technology assists with repetitive and mundane tasks. Document annotation has many advantages for both large and small companies and it is imperative that more organizations invest in this technology. Automated annotation platforms and solutions have made it easier and more affordable for everyone to participate in the process of document annotation and as such the use of the document, annotation is set to continue increasing in the future.

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