Named-entity Recognition (NER)


What is Named-entity Recognition? 

Named-entity Recognition (NER) is a method of recognizing and classifying essential pieces of information from within larger unstructured text-based data into predefined categories such as person names, organizations, locations and more.

How Does Named-entity Recognition Work?

Named-entity recognition (NER) is a natural language processing technique used to pinpoint and classify named entities found in unstructured text and convert them into predefined categories such as names and locations. 

Named-entity recognition works by taking an unannotated section of text, such as:

Ryan sold 400 shares of in 2021.

And annotating it so that it highlights the different entities and names in the section:

[Ryan]personsold 400 shares of []organization in [2021]time. 

Various machine learning algorithms are typically used for NER, such as support vector machines and conditional random fields, as well as newer techniques like Recurrent neural networks (RNNs) and transformers (e.g., BERT).

Use Cases

Named-entity recognition is one of the most fundamental problems in NLP and has a variety of applications.

One of the most common areas where we can find named-entity recognition being used is within web search engines in order to understand the entities within a query. 

In brief, search engines aim to understand what the user is searching for and give them the best answer to their query. The way in which a search engine does this is by categorizing the content on a web using 'keywords' that are “understood” using NER

Another commonly seen application of NER is within product reviews. Named-entity recognition is used in order to extract product names and other important entities from online reviews. By doing this, people can gain insights on which of their products and features are being discussed and in what way.

A third use of named entity recognition is within chatbots

Named-entity Recognition and Chatbots 

As mentioned, named-entity recognition helps identity and categorize critical elements within textual data. This enables a lot of businesses to find insights from large unstructured datasets.

One way in which an organization could capitalize from the use of named-entity recognition is through the use of chatbots

Many businesses set up chatbots to help automate tedious processes such as documentation collection, customer support services and more, in order to save manual work for other complex tasks.

There are, however, a few challenges involved in doing this, specifically in terms of customer support services. Firstly, the chatbot must understand who it is speaking to in order to provide a good user experience. Secondly, they have to be able to extract important data in order to respond in a meaningful way.

These challenges can only be overcome if the chatbot is thoroughly trained with named-entity recognition algorithms to recognize these entities from within a chat and process them correctly. 

Furthermore, previously extracted information from named-entity recognition models can be used to further train the algorithm and attain higher accuracy levels.  

Below is an example of NER classifying named entities within a conversation of Hyro’s COVID-19 virtual assistant. In this specific case, the named-entity recognition has been trained to recognize medical symptoms from within a chat, such as coughing, and use additional knowledge about COVID-19 specific terms, such as identifying Rome as a high risk city.


                                                     Hyro’s Named-entity Recognition 

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Conversational AI
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Named-entity Recognition (NER)
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