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Extracting useful information from unstructured data is simply put crucial for creating commercial success. Named Entity Recognition (NER) is an important tool for this, allowing businesses to identify and classify entities within text data. This can help organizations unlock valuable information. Here, we explore the impact of NER in various industries and provide examples of its successful use.


What type of information, you might ask? It could be gathering information from customer communications, social media etc that can be used to improve services or even products. It can also be used to reduce interruptions in the supply chain or map disease patterns in life science. You can read more about business use cases further down but let us first give a brief explanation of Named Entity Recognition (NER).

NER is a natural language processing (NLP) technique that identifies and classifies entities in unstructured text. These entities can include people, organizations, locations, dates, and numbers. Using advanced machine learning algorithms, NER systems analyze text to find specific words or phrases that match predefined entity types. This involves extracting language features and applying statistical models to identify patterns.


Business Use Cases of Named Entity Recognition

Named Entity Recognition (NER) is useful in many industries and business support functions, offering solutions to various business challenges. Here are some examples of how NER adds value and drives innovation:

Improving products, marketing and customer relations

NER improves CRM (customer relationship management) by analyzing unstructured data to extract and categorize customer mentions, feedback, and inquiries from emails, social media, and reviews. This helps organizations quickly process large amounts of data and understand customer sentiment, preferences, and issues. Companies can use NER to handle customer inquiries efficiently and identify areas for product improvement. For example, a manufacturing company used NER to identify key information in customer emails about product issues, improving inquiry response times and product quality.

Retailers can leverage NER to analyze customer interaction data from multiple sources, improving data integration and providing actionable insights for marketing and customer service teams.

In marketing, NER can be used for targeted ad campaigns by analyzing social media for brand mentions, competitors, trends, influencers, and consumer opinions. For instance, a global retail company might use NER to gauge campaign effectiveness and understand customer sentiment about new products. By identifying positive feedback, marketers can focus on highlighted features in their ads. NER also helps find potential influencers for collaborations, extending the reach of marketing efforts.

Optimizing the supply chain

In supply chain management, NER helps optimize logistics, inventory management, and procurement for both retail and manufacturing. By processing textual data from purchase orders, invoices, shipping manifests, and procurement contracts, organizations can identify and categorize key entities like product names, suppliers, quantities, and delivery locations.

Access to this structured data enables real-time inventory tracking, demand forecasting, and supplier management. For instance, a global retail logistics company used NER to extract shipping information from bills of lading, improving shipment tracking and route planning. Similarly, a manufacturing firm used NER to optimize inventory management and reduce supply chain disruptions.

Optimizing patient care in life sciences

In the life sciences industry, NER has significant potential for speeding up research, development, and patient care. By extracting and categorizing medical entities from clinical notes, research papers, and patient records, organizations can streamline workflows and make data-driven decisions.

For example, a life sciences firm used NER to extract patient demographics, medical conditions, and treatment details from electronic health records (EHRs). This helped researchers identify disease patterns, optimize treatments, and improve patient care.

Automating work in Finance departments

In finance departments in businesses in all industries compliance with regulations and risk mitigation are crucial. For example, a retail company reviewing numerous financial agreements for compliance can use NER to automate the extraction of key entities like transaction amounts, interest rates, vendors, and contractual parties.

This automation helps the finance department quickly and accurately process financial documents, ensuring compliance with regulations. By identifying and categorizing entities within these documents, the finance team can reduce financial risks, monitor transactions, and make informed financial decisions.

Making Human Resources (HR) more efficient

NER enhances HR processes by improving recruitment, onboarding, and workforce management. For example, NER can analyze resumes and employee records to extract skills, job titles, and qualifications, making recruitment faster and more data-driven. A global tech company might use NER-powered tracking to sort resumes and identify suitable candidates quickly.

NER simplifies employee onboarding by pulling key information from documents like contracts and tax forms, reducing manual data entry and errors. It also helps manage employee feedback and performance reviews by categorizing and analyzing comments and ratings. This enables HR to identify top employees, address weaknesses, plan succession, track employee sentiment, and improve workplace satisfaction. Overall, NER enhances HR efficiency and employee experiences.

Improving the legal part of business

In all industries legal teams deal with large amounts of textual data, including contracts, regulatory documents, and case precedents. NER can revolutionize information extraction by automating the extraction of vital information like contract clauses, legal statutes, and parties involved, streamlining legal research and contract reviews.

For example, a manufacturing company's legal department used NER to analyze patent documents efficiently. By automating the extraction of key entities like inventors and technical details, the legal team sped up patent prosecution, reduced IP disputes, and provided strategic insights to the company. NER also enhances e-discovery by identifying relevant entities in electronic information, reducing manual review efforts and speeding up case timelines.

Four types of NER systems based on their methods

This part of the blog is for those of you who are really interested in technology and the different NER systems. Here you will get a short introduction and an overview of advantages and disadvantages.

Rule-Based NER Systems: Rule-based NER systems use predefined rules and patterns to find named entities in text. These rules are based on language patterns, regular expressions, and dictionaries.

Advantages: Easily customizable and understandable.

Disadvantages: May not work well with new data and can be time-consuming to create and maintain.

Statistical NER Systems: Statistical NER systems use machine learning algorithms like Hidden Markov Models (HMMs), Conditional Random Fields (CRFs), or newer deep learning methods like Recurrent Neural Networks (RNNs) and Transformer-based models (such as BERT, GPT). These models learn to recognize entities based on patterns in labeled data.

Advantages: Generalize well to new data and capture complex patterns.

Disadvantages: Need a lot of annotated training data, and performance may drop with out-of-domain or noisy text.

Hybrid NER Systems: Hybrid NER systems combine rule-based and statistical approaches to use the strengths of both. For example, a hybrid system might use rules for certain entities and statistical models for more general entities or those with less predictable patterns.

Advantages: Can achieve higher accuracy by combining different approaches.

Disadvantages: More complex to implement and require careful tuning.

Deep Learning NER Systems: Deep learning NER systems use neural network architectures to automatically learn features and patterns from text. Models like Bidirectional LSTMs (BiLSTMs), Convolutional Neural Networks (CNNs), and Transformer-based architectures (e.g., BERT, GPT) have been very successful in NER tasks. They learn distributed representations of words and context, improving NER performance.

Advantages: Handle complex contexts and have achieved top performance on various NER benchmarks.

Disadvantages: Require large amounts of labeled data and are computationally expensive.

Each type of NER system has its own pros and cons. The choice of system depends on the specific task, available labeled data, computational resources, and desired performance. Columbus can advise on the right solution for your business.

Broad and transformative insights

In conclusion, Named Entity Recognition (NER) is a key technology with significant benefits for businesses in various industries. By automatically extracting and categorizing entities within text data, NER helps organizations gain valuable insights, streamline operations, and drive innovation. The applications of NER are broad and transformative. As organizations continue to use NER, they are set to gain a competitive edge in a data-driven world, driving growth and delivering more value to stakeholders.



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