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Named Entity Recognition Software

Knowledge Base Software

Named Entity Recognition Software

Use Named Entity Recognition Software in your business to speed up processing data. We are entering the era of machine learning and artificial intelligence, also one of the areas is Entity Recognition software they have unprecedented opportunities to streamline every aspect of their operation.

Those business processes that used to be time and effort consuming now can be done quickly and efficiently by a computer.

One of the ways to dramatically improve performance is entity recognition software. Among other things, it helps ease the workload, saves time on data processing, and provides a new perspective on information.

What is Entity Recognition?

Entity recognition has many names – named entity recognition (NER), entity identification, entity name extraction, and even entity chunking.

Whatever you call this technology, it all comes down to the same thing – it is software that fetches certain words (entities) in the text and elaborates on their meaning or provides more related information.

Anything can be used as a trigger word – people’s names, places, addresses, job titles, brands and companies, dates, currencies, and essentially everything that you deem important.

More often, people have to deal with unstructured texts. The breakdown of content into paragraphs and headers is great but you can bring even more order along with semantic information to your texts thanks to entity recognition.

Its mission is to supply every significant word with a detailed explanation or additional facts. Your readers don’t have to spend time looking for clarification or answers to their questions elsewhere – everything they need is already featured in this document as in-text links and tips.

For an end-user, entity name extraction might be similar to footnotes. However, it is designed in a more convenient and informative way.

Named entity recognition considerably speeds up the understanding and subsequent processing of data. It reveals what kind of information a file or piece of writing contains and provides a new look at data from a reader’s perspective.

In addition, this technology works in the same harness with machine learning algorithms. It can become one of the stages of data management as part of the natural language processing system.

This is How Entity Extraction Works

Named Entity Recognition also known as entity extraction software analyzes a text and finds words that fit the description of entities. Then, the software pulls the information related to these words from the pre-installed database and creates the corresponding links and connections.

Databases are normally divided into several categories such as People, Organizations, Places, etc. Furthermore, each category can be as deep as necessary for a specific client and project.

For example, People may have subcategories Employees, Suppliers, Partners, etc.

On top of that, users are able to additionally create their own custom entities. Let’s say you are a manufacturer of skincare products.

You can specify entities for Brandname night cream and Hyaluronic acid, which are attributed to Product and Component categories respectively.

Each time such words are mentioned in product descriptions, reviews, feedbacks, etc. entity identification software will draw extra information from the appropriate database categories.

Entity recognition does not just look for words that have corresponding entries but also understands the context they are used in.

For example, if a text contains the word ‘smith’, the software comprehends whether it refers to a person whose profession is a blacksmith or if it is an individual’s last name.

Natural Entity Recognition
Natural Entity Recognition

Examples of Entity Recognition

The best way to describe how entity recognition works is to illustrate it with a specific example. Let’s take a look at this piece of text:

“Spending on education is the second-largest budget item of the United Kingdom after healthcare. As of 2019, the government invested £91 million in education, which is 4.2% of national income.”

What you see as a regular text, entity recognition software sees as a number of trigger words:

  • ‘Education’ refers to Public Service;
  • ‘the United Kingdom’ relates to Country and Location;
  • ‘2019 belongs to the category Date and Time;
  • ‘91 million’ are classified as numbers.

In addition to general categories from the pre-installed database, you can customize entity recognition to elaborate on who heads the UK ‘government’, what other ‘budget items’ are, what ‘national income’ the UK had in 2019, and so on.

How the extracted entities can be used?

The extracted entities can be exported to any kind of software via RESTful API. The Entity Recognition cloud software users have access to the RESTful API. The API is described in the documentation provided for all our customers.

How Can You Use Entity Recognition Software?

In a nutshell, entity recognition can be of great use anywhere as long as texts and data are involved.

Take, for example, news and online publishing. These industries produce a huge amount of content. Naturally, they want it to be processed correctly.

Here’s how entity recognition software can help with content management – it will scan publications and highlight the most frequently mentioned names, dates, etc.

Based on this information, news agencies can create correct tags and deliver articles right to the target audience.

In a similar way, entity identification helps handle and structure blog posts. After analyzing content on your digital platform, it is able to pick out trigger words so that you can apply relevant tags.

This will aid you to categorize your posts. Accordingly, your readers can easier find information on topics they are interested in. Besides that, entity recognition can generate recommendations based on previously published posts.

Needless to say, NER-enhanced content is capable of providing much more information than a similar piece deprived of an entity recognition feature.

Named entity recognition is beneficial for every business that works with consumers since it is adept at streamlining customer support. On the one hand, it provides in-depth information for customers regarding technical terms and other concepts requiring explanation.

On the other hand, in the process of communication with customers, this software can capture data concerning product names, features, service regions, etc. which will help to transfer a request to the appropriate department.

How Can We Help?

Every entrepreneur dreams of automating as many processes as possible – entrusting customer support to chatbots or handing over document and database management to artificial intelligence. Thanks to Knowledge Base Software, this dream becomes a reality.

You no longer need to spend too much on stuff recruiting and training to ensure the normal operation of a customer support department. Artificial intelligence can cope with these tasks as well as real people.

Our knowledgebase software, chatbot development, and entity recognition software are comprehensive solutions to automate, optimise, and improve business processes.

Courtesy of Knowledge Base Software, you are able to process data, solve problems, and make decisions in a fast and professional manner.

Please take a look at how Named Entity Recognition works in the pharmaceutical business also watch the demo video how the software works

Also, more about the entity recognition can be found in Wikipedia