NER allows machines to extract or identify entities automatically. It’s an advanced Artificial Intelligence-powered technology that identifies crucial concepts in unstructured texts. Then, it turns them into data that’s semantically structured. Search engines use it to comprehend queries, while chatbots use it to engage with humans. A good entity extraction software provides quick, scalable, and accurate extraction in numerous languages by utilizing AI-based Machine Learning and Natural Language Processing technologies. You can deploy it on the Cloud and on-premises and enable various Big Data Text Analytics applications.
What can NER do?
Extracting entities is a text analysis method that utilizes natural language processing to pull particular data from unstructured text. It also categorizes it as per predefined categories. These categories are named entities. They are the words or phrases that display a noun. It consists of proper names and also numerical expressions of quantity or time like phone numbers, dates, and monetary values.
You can understand entities better with the help of an example.
- HP, a US-based multinational company, was started by two American engineers, Bill Hewlett and David Packard. On April 28, 2010, it announced the buyout of Palm, Ink. for $1.2 billion.
In this simple example, HP is the name of a company. The US is the location, while $1.2 billion is the monetary value.
This technique allows people to figure valuable information in vast amounts of unstructured text data in business. Going through numerous surveys, customer support tickets, service reviews, and emails can consume endless hours of manual labor. But with the automated NER technique, it is possible to acquire the data you require in a couple of seconds. This technique has various applications. They are described in the subsequent section.
Applications of NER
Insights into how customers view service or product
Acting on customer feedback is essential to improve and find your areas of strength. When you analyze product reviews, posts on various social media channels, and survey responses, you can figure out how your customers see your brand. NER allows brands to quickly detect when people are mentioning their names. For example, you might be interested in feedback that mentions your competitors and particular products related to your business. You can easily combine NER with sentiment analysis to obtain insights into customers’ perceptions of other services or brands.
Getting the needed data from customer support tickets
For a growing business, keeping up with customer support tickets is difficult. Although you can use helpdesk software to handle and arrange all tickets in a single place, you also need to make sense of them. NER tools allow automated ticket tagging. You can use them to extract the required data within your tickets, like shipping date, product type, and serial numbers. Not only that, but you can also obtain emails, company names, and URLs. You can then employ this information for categorizing tickets and suitably routing them.
Automation of personalized recommendations
The NER technique is abundantly utilized to automate personalized content recommendations. Companies like Netflix and Amazon use this technique to provide content to their customers according to their preferences and behaviors. It thus keeps them continuously engaged. By detecting entities in a product description automatically, recommendation systems can identify other products having similar entities. Its implemented in various industries like eCommerce and news publications.
How to Carry out NER?
A reliable entity extraction software has over a hundred types of entities and provides a broad semantic ontology for extraction. It incorporates people, different organizations, kinds of places, addresses, phone numbers, and the like. Such software not only executes extraction but also allocates normalized forms to extracted person, place names, organizations while considering capitalization, abbreviation, acronym, etc.
A user-friendly extraction solution can integrate well with popular search, BI tools, and geospatial. It thus makes your routine tasks more efficient and saves a lot of time that may otherwise get spent in manual processing.