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The process of documentation is an essential part of running any business. Reliable documentation is critical for day-to-day and long-term operations in several industries, including healthcare, technology, and the food industry. Kili-technology is a reliable document annotation tool platform that can significantly reduce the cost and time requirements.
The process of document analysis is laborious and prone to making mistakes. If it is done manually, there is a possibility that errors can occur, which will ultimately lead to inefficiencies for the business. Documents that have been annotated can help reduce this problem.
This article aims to assist business leaders in maximizing the return on their efforts in data annotation by providing an analysis of document annotation, including its categories, use cases, and practice guidelines.
Document Annotation: How Does it work?
Annotating documents involves assigning labels to the various pieces of information and values contained within them to train a machine learning model. This model then feeds this knowledge into an AI-enabled document processing system. This system can organize, process, and show this data in a manner that is customized to the needs of the user.
As the prevalence of digitization increases, document annotation becomes increasingly vital. This is particularly significant for historical materials. Documents are now downloadable due to digitization, and their contents may be studied and classified much more easily with the help of document annotation.
Some of the data is disregarded and forwarded on to be examined by a human annotator, who subsequently modifies the training model to better handle situations of a similar nature in the future.
What Kinds of Document Annotations and Applications are There?
Various sorts of document annotations are discussed, as well as some examples of how they can be put to good use in the real world.
Named entity annotation
Named Entity Recognition (NER) technology is used to apply labels to specific words and phrases in the data. In cases where a machine learning model needs to learn the content of the written text, NER is employed. Examples of possible applications:
- Automated customer service channel inquiries to the appropriate channels. As a result of the AI model, specific phrases are linked to specific channels.
- It is possible to train ML models using NER document annotation to find resume material that matches job requirements.
- It is possible to use NER document annotation in healthcare to train machine learning models to understand patient information and clinical records.
Sentiment annotation
Annotating the text in such a way as to highlight its emotional significance is an example of this form of annotation. Annotation of sentiment can assist in training a machine learning model to comprehend if the feeling or attitude conveyed by the phrase is one of positivity, neutrality, or pessimism.
Use cases:
- Annotation of sentiment can be used in marketing campaigns conducted via digital or social media to comprehend better the meaning of the feedback provided by customers and form a more accurate picture of the company’s reputation.
- Annotation of emotion is a tool that may be utilized in the field of human resources to analyze a large number of employee satisfaction questionnaires.
- Sentiment annotation allows AI models to better comprehend the mood behind consumer contacts like reviews, e-mails, and instant conversations. Automotive annotation can help analyze customer sentiments towards specific car models or brands based on their reviews and feedback.AI systems can autonomously direct requests to particular departments or individuals by reading and evaluating these communications and looking at the sentiment behind a particular message.
Semantic document annotation
Labeling jargon and unspecific terms are required components of this sort of annotation. The usage of virtual assistants and chatbots to better interpret consumer inquiries that contain jargon is one example of a typical use case.
What are Some of the Most Effective Methods For Annotating a Document?
The following are some industry standards that are recommended for use in document annotation projects:
Annotate everything
Annotating a document effectively requires performing a job that is complete. A machine learning model can learn from both positive and negative instances and from annotated and unannotated versions of the same document. For instance, if out of ten documents, only the first five are annotated while the remaining five are not, then the machine learning model will learn to ignore the data in the remaining five documents. This is because the first five documents were annotated.
Maintaining coherence at all times is essential
When annotating documents, correctness isn’t always as crucial as maintaining a consistent style. For instance, when annotating the paperwork for car registration, there may be multiple name variations, such as Civic and Civic XR. It is not necessary to know which name should be used when labeling something; it is sufficient to choose one randomly and continue the labeling process.
Include seasoned annotators in the process
Because massive datasets call for specialized knowledge, it is advantageous to collaborate with knowledgeable annotators throughout the process. Annotators with more experience can assess the annotation and provide input on it.
Ensuring the Accuracy of Document Annotation
Annotations, like most other aspects of life, require careful attention to detail to achieve the highest quality of work and the most satisfying outcomes. Many other approaches can be used to accomplish this, but the following are the most common ones:
- Annotation on Multiple Copies
It is generally accepted within the academic community that texts annotated on multiple occasions are of a higher quality. Because of this, texts are frequently marked up to three times by various individuals, resulting in more minor personal annotations and higher quality.
- Inter-Annotator Agreements
It is possible to ascertain whether or not a particular annotation is accurate by calculating the degree to which different annotators agree. If there is an agreement between two or more annotators, but one expresses a different opinion, it is often safe to assume that the annotators who reached a consensus have reached the proper conclusion.
Bottom Line
Document annotation offers many benefits to businesses of all sizes, and it is essential that more enterprises invest in the technology behind it in order to reap those benefits. As a result of the widespread availability of automated annotation platforms and solutions, participation in the document annotation process is now more straightforward and more economical for anyone. As a result, the use of document annotation is anticipated to remain to expand in the coming years.
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