Named Entity Recognition (NER) adds structure to unstructured data
Most of the data available to companies is unstructured and therefore more difficult to access, process, and exploit. Named Entity Recognition (NER) is a powerful Natural Language Processing (NLP) technique that adds structure to unstructured data by classifying named entities in one or more predetermined categories. These entities can be names of people, brands, organizations, places, interests, and more.
Example of named entities in a text:
“The AEG washing machine costs 550 euro on Amazon.”
AEG = brand
Washing machine = product
550 euro = monetary value
Amazon = retailer
Named Entity Recognition is very different from and much more powerful than a simple keyword search as it does not only tag a specific keyword, but also related words and concepts as learned through Machine Learning (ML).
In order to recognize entities, the NER model must learn from training data — chunks of text with manually defined entities are fed to the model until it starts recognizing patterns and identifying entities automatically. The model will not only recognize the defined text but also variants, synonyms, and related concepts.
As an example, assume we want to categorize news related to The Coca Cola Company. A keyword like “The Coca Cola Company” or “Coca-Cola” would tag some of the news correctly, but thanks to machine learning, Named Entity Recognition would tag also news that mentions “Fanta,” “Fuze Tea,” “Sprite,” “Costa Coffee,” “Innocent,” “Powerade,” and many other brands owned by the company, including variants and misspellings.
Once the model has been trained, entity recognition is very accurate and makes searches more effective, processes more efficient, and experiences more relevant and personal.
Named Entity Recognition (NER) adds structure to unstructured data
Most of the data available to companies is unstructured and therefore more difficult to access, process, and exploit. Named Entity Recognition (NER) is a powerful Natural Language Processing (NLP) technique that adds structure to unstructured data by classifying named entities in one or more predetermined categories. These entities can be names of people, brands, organizations, places, interests, and more.
Example of named entities in a text:
“The AEG washing machine costs 550 euro on Amazon.”
AEG = brand
Washing machine = product
550 euro = monetary value
Amazon = retailer
Named Entity Recognition is very different from and much more powerful than a simple keyword search as it does not only tag a specific keyword, but also related words and concepts as learned through Machine Learning (ML).
In order to recognize entities, the NER model must learn from training data — chunks of text with manually defined entities are fed to the model until it starts recognizing patterns and identifying entities automatically. The model will not only recognize the defined text but also variants, synonyms, and related concepts.
As an example, assume we want to categorize news related to The Coca Cola Company. A keyword like “The Coca Cola Company” or “Coca-Cola” would tag some of the news correctly, but thanks to machine learning, Named Entity Recognition would tag also news that mentions “Fanta,” “Fuze Tea,” “Sprite,” “Costa Coffee,” “Innocent,” “Powerade,” and many other brands owned by the company, including variants and misspellings.
Once the model has been trained, entity recognition is very accurate and makes searches more effective, processes more efficient, and experiences more relevant and personal.
Add structure to unstructured data using NER
Industry & Competitor Intelligence
Market intelligence teams collect a lot of information about the industry and competition. Collected information covers a wide variety of companies, brands, products, countries, and topics from product launches to management changes, acquisitions, divestures, investments, partnerships, patents, and more.
NER makes it possible to automatically recognize entities in qualitative content which greatly reduces the time required to process data and improves the quality and relevancy of the information obtained for further use by the market intelligence teams.
Customer Reviews
Online reviews are a great source of customer feedback — they can provide rich insights about what clients like and dislike about brands, products, and services.
NER systems can be used to organize all this customer feedback and pinpoint recurring problems. For example, if you are a restaurant chain, you could use NER to detect locations that are mentioned most often in negative customer feedback, which might lead you to focus on a particular branch.
NER adds structure to unstructured documents.
Add structure to unstructured data using NER
Industry & Competitor Intelligence
Market intelligence teams collect a lot of information about the industry and competition. Collected information covers a wide variety of companies, brands, products, countries, and topics from product launches to management changes, acquisitions, divestures, investments, partnerships, patents, and more.
NER makes it possible to automatically recognize entities in qualitative content which greatly reduces the time required to process data and improves the quality and relevancy of the information obtained for further use by the market intelligence teams.
Customer Reviews
Online reviews are a great source of customer feedback — they can provide rich insights about what clients like and dislike about brands, products, and services.
NER systems can be used to organize all this customer feedback and pinpoint recurring problems. For example, if you are a restaurant chain, you could use NER to detect locations that are mentioned most often in negative customer feedback, which might lead you to focus on a particular branch.
NER adds structure to unstructured documents.
Improve process efficiency and effectiveness using NER
Customer care
Customers write emails or use chat to get support with their order or product.
NER recognizes the type of support required and companies can provide relevant content to resolve the issue quickly and efficiently.
NER helps to reduce cost and improves customer satisfaction with a faster resolution.
Improve process efficiency and effectiveness using NER
Customer care
Customers write emails or use chat to get support with their order or product.
NER recognizes the type of support required and companies can provide relevant content to resolve the issue quickly and efficiently.
NER helps to reduce cost and improves customer satisfaction with a faster resolution.
Personalize customer experiences using NER
Content Personalization
People are individuals with different preferences and tastes. By identifying their preferences and using NER to profile content, products, and services along the same dimensions, it is possible to uncover the most relevant content, products, and services that will delight your customers, and increase their engagement and conversion.
For example, companies like Netflix and Amazon can make very relevant recommendations by combining the knowledge of what you searched, watched, and/or purchased, and the characteristics of alternative or complementary products, services, and content mapped using NER.
NER makes it possible to personalize content, products, and services to meet individual needs.
Want to learn more or discuss your project?
Personalize customer experiences using NER
Content Personalization
People are individuals with different preferences and tastes. By identifying their preferences and using NER to profile content, products, and services along the same dimensions, it is possible to uncover the most relevant content, products, and services that will delight your customers, and increase their engagement and conversion.
For example, companies like Netflix and Amazon can make very relevant recommendations by combining the knowledge of what you searched, watched, and/or purchased, and the characteristics of alternative or complementary products, services, and content mapped using NER.
NER makes it possible to personalize content, products, and services to meet individual needs.
Want to learn more or discuss your project?