Intellectual property is one of the few unique assets in your business. Entity extraction has the potential to hugely increase both the quantity and quality of your information.

Entity extraction is the process of giving structure and semantic information to the vast unstructured data your enterprise deals with daily.

It takes meaningful information out of unstructured documents and classifies them into pre-defined categories (person, place, organization, etc.).

Potential areas where it can be applied vary from amplifying research, performing smart searches, and improving diagnostics in healthcare, to extracting vast amounts of financial knowledge out of unstructured texts.

What is an entity?

An entity is anything that has independent and unique existence.

Entities may be persons, companies, locations, dates and times, monetary values, etc.

Consider this simple sentence:

Datavid is a data intelligence company, founded by Balvinder Dang and Silvia Rausanu in 2018, and is headquartered in the UK.

The entities in the above sentence are:

• Company name: Datavid
• Person names: Balvinder Dang, Silvia Rausanu
• Date: 2018
• Geographic location: The UK

Entities are the most important part of the text, mostly appearing as nouns, pronouns, numbers, or even entire sentences.

Understanding the entity extraction

Simply put, entity extraction is a 2-step process:

• Identifying entities out of raw data, and;

• Grouping them under predefined categories.

These entities are then matched against predefined categories for semantic analysis.

Most of the previously unstructured data become structured, and the information held within them is used to its full potential.

Entity extraction is also known as entity identification, entity chunking, and named entity recognition (NER).

How entity extraction works

You use unstructured documents as input to extract entities.

Then, you define the classification system that you wish to use.

You can extract a particular entity type or multiple entity types.

This is the most important step—how you choose to classify an entity will change the context and the relationship with other entities.

Consider ‘Apple’ to understand better.

It can be classified as a ‘company’ or ‘fruit’. The meaning of the named entity ‘Apple’ doesn’t change but when you classify it as a company—the whole context shifts.

Entity extraction: Use cases across industries

Entity extraction can be applied to the huge and complex worlds of finance, medicine, publishing, and many more.

Entity extraction in finance:

If your enterprise belongs to the BFSI industry (Banking, Financial Services, and Insurance), you receive loads of documents via disparate sources in various formats.

You might be using spreadsheets to generate important financial documents.

(P&L statements, A/c payables, receivables, etc.)

There is a lot of valuable data in documents without a fixed format.

But as your data sources grow, it becomes difficult to find and make use of such data.

To solve this problem, you find the entities and group them under pre-defined categories.

Your entity type could be products or services, financial metrics, amount, etc., and then you identify the relationship between the entities.

You will be able to map the dollars spent gained on a product or service to the right metric it belongs to.

The goal is to pre-train the financial entity extraction model on the entities of your concern.

Then apply the concepts they are associated with on a sample and later use it on a larger set of unstructured documents.

By using entity extraction, you speed up the process of extracting financial insight for better decision-making.

Entity extraction in healthcare

Hospitals and medical institutions generate electronic medical records (EMRs) during patient diagnosis and treatment.

To use such a rich and vast healthcare database, medically-named entity recognition is applied to structured reports, healthcare research journals, and scans to create a medical domain entity dictionary.

Later on, this entity dictionary is used for categories of medical entities.

These could be disease and diagnosis, drug and effects, laboratory reports, surgery, etc., and then the relationships are derived between them.

The pre-trained model created above is applied to unstructured data to extract the info that lay unused.

The entire process enriches you with information not easily available otherwise, speeding up the clinical diagnosis process, improving the patient’s experience and quality of service, and enhancing the medical knowledge base.

Entity extraction in publishing

In the research and publishing space, the vast archive consists of literature on research articles in XML and non-text formats.

It consists of journals, books, and posters (pdf with images)from internal teams and partners.

You are either working on a research paper that requires you to keep referencing such content, or you’re in a space where you have to make federal sponsored research public, including all the important facts and figures to support the conclusion.

Entity extraction proves useful to ensure that you have the required data to work on.

It can scan your data repositories to extract entities and group them under categories like the author, topic, industry, pub date, etc.

This helps with faster storage and retrieval.

Besides, you can organize under defined hierarchies, which is helpful for research.

Starting on the entity extraction journey

Entity extraction helps you leverage the potential of your data that usually remains unused in the huge amount of unstructured texts and documents available to you.

Recognizing and tagging entities can be applied to any domain if your enterprise has large data set of unstructured documents.

Manually extracting entities is not an option because the understanding varies from one individual to another.

There are tools available using either pre-built or custom models to extract entities.

With tools like Datavid Rover, our knowledge engine, you can track down all of your enterprise’s undiscovered data.

It will not only identify occurrences and trends across entities but also uncover and visualize the relationship they hold through knowledge graphs.

Simplify the processing of adding intelligence to your data by extracting entities.

Frequently asked questions