Named entity recognition is a subset of NLP (Natural Language Processing). Yet for some odd reasons it is often treated as a separate entity. To clarify the situation, and explain what NER stands for in NLP and the benefits it offers as a technique, we have written this article. Read ahead to learn more…
A whole lot of developers often tend to confuse the role of Named Entity Recognition(NER) in Natural Language Processing. It is because they are so closely related and intertwined that it often becomes difficult to distinguish between the two (in terms of functionality).
To clear this confusion and help you understand “What is Named Entity Recognition in NLP?”, we have written this article.
Named Entity Recognition is a technique of natural language processing that is used for the categorization of the data. It is a subfield of Artificial Intelligence(AI) and is being used heavily in the industries today to automate major categorization of data for unstructured text and datasets.
Suppose a working model is required to categorize name, organization, and job profile for finding the relevant candidate from a job portal. The task at hand is to categorize data as mentioned above from the bio of the candidates.
Taking an example for the bio i.e. “John has been working at Amazon as a Full Stack Developer”.
Now from a human perspective, it is easier for any HR to understand the details about John i.e. name, organization, and job profile. But what if there are thousands of bios to checkout for a relevant candidate? This is where NER comes in.
The single sentence from the bio of John will be processed by a NER model such as this:
John[name] has been working in Amazon[organization] as a Full Stack Developer[job profile].
Named entity recognition automatically categorizes data from text based on its previous training. Its applications are huge because it is machine based and doesn’t get affected by tons of work leading to cognitive overload.
Depending upon the use case and functionality, there are several types of NER systems one can adopt for development. These are:
This is the most primitive type of NER system used today. It is pretty simplified and takes advantage of a dictionary that needs to be regularly updated. Every time data is fed to this type of system, the NER model will provide a response using a string matching algorithm.
In this type of system, there are predefined rules for data extraction and categorization. The model basically works on the morphological sequence of a sentence (the way inflections are used in sentences) and context.
For example: Mr. John went to purchase the grocery.
In this example, rule-based NER will easily detect that John is the name because names are generally followed by titles such as Mr., Ms., Mrs., etc. A rule-based NER allows the developer to create such rules and deploy them into their developing model for enhanced data extraction.
The output in this type of model is based on statistics. An ML model for NER is pre trained to extract and categorize data. The learning ideally happens in three formats i.e. supervised, unsupervised, and semi-supervised.
In a machine learning based NER system, the systems are ideally trained with annotated data. The learning of ML based systems happens quite identical to a child that starts to learn by going through multiple examples. With multiple iterations at hand, the model automatically starts to categorize data just like a human would.
Deep learning as we know is a subfield of machine learning and is a level ahead in terms of application. It offers considerable advantages over the previous system mentioned. In essence, deep learning is a field of AI that deals with artificial neural networks. With this capability in the NER model, the model is capable of processing loads of unstructured data that is otherwise difficult to do. The model has the capacity to understand complex inputs and provide answers. Deep learning is used for building state-of-the-art models that are nothing short of intuition and cognitive. With each iteration and experience, the model becomes more robust and provides higher accuracies over time.
Understanding NER in reference to NLP (natural language processing) is pretty simple. It is one of the key detection techniques used in NLP. To further explain, the capability of a system to identify key elements or entities such as name, organization, designation, time, experience, etc. comes under the bracket of NER. Without NER, data extraction using NLP will be devoid of any context that is required to understand a phrase, a sentence, or an article to effectively process that data.
By now we have explained what an NER based model does and is capable of doing. To further add, it is important to understand application of NER in context to industrial usage. Below are some of the few ways NER can be used:
With NER, it is possible to search key elements or entities from a phrase, sentence, or a paragraph. This capability can be used during online searches to find elements such as name, organization, events, locations, etc. from thousands of articles which otherwise would require tons of resources at hand.
Right now, we have virtual assistants, chabots, or RPAs for process automation. With each industry having their own set of requirements, the operations require process automation that can’t be further lubricated without context.
We can also take the example of auto-responses. Suppose a query received in an email from a customer says “The software provided by your organization intermittently stops and can only be accessed once the computer is rebooted”. A custom made NLP model would scan the data for relevant words and provide an auto-response that is already provided within the system. And if the operation of scanning doesn’t find any relevant data, it can produce and forward a generic answer to the customer such as “Our representative will reach you shortly”.
A great example of this would be Netflix. As we know, Netflix is big on providing great movie recommendations based on your previous viewing experience. Their content recommendation system would simply check the synopsis of the movies and look for entities that are similar to other content on the platform. It could be the movie genre, actors name, director’s name, words in movie synopsis like cop drama, court drama, romance, or any other key entity that is common within your previous viewing. This can be achieved using a deep learning model that is capable of understanding the users requirements without the user telling the system.
Annotation of data is done via labeling it. It can include processes such as text identification, text analysis, concept extraction, indexing, storing, etc. Each of these processes helps in identifying text and labeling it as per the usage. For instance, an automated system for labeling the article on a forum can be created. The NER model would simply checkout text in the article, label it, and based on that it can identify which article belongs to which genre such as technology, lifestyle, spiritual, philosophy, etc.
There are several benefits of using Named Entity Recognition. These are:
The most common language that is used for developing applications and softwares that use NER is python. Adding to it, Java and C++ are two other very common languages that are used for developing NER models.
In order to get started with NER models developers require APIs to work with. These APIs can be further classified into two types:
The best way to start developing an NER based application for an independent developer or an organization developing a proprietary application is by using open-source APIs. These APIs don’t require a license, they are free to use, and don’t have a hard learning curve.
Below are some of the top open-source APIs for NER models. These are:
These are backed by organizations and entities that might demand the developer to have a license. These APIs often cater to a niche and can be termed as finished products that can be used right away for a specific purpose.
Below are some common examples of SaaS based NER APIs. These are:
To learn more about these APIs, click on the link provided here.
Intelligent Document Processing (IDP) platforms are AI-based document processing solutions. These platforms have the capacity to process unstructured documents at a very large scale. Without the inclusion of Named Entity Recognition, it is not possible to create an IDP platform. Why? With unstructured documents comes a wide range of entries that signify the same data but are written differently in different documents.
The best example of this would be how job profiles can be written in different resumes but means the same. For example Job Title, Designation, Job Role, etc. Without NER, an artificial system won’t be able to determine that each of these data points means the same.
VisionERA is an IDP platform that is built using proprietary advanced technologies. One of the core technologies utilized by VisionERA is NLP (Natural Language Processing). With NLP, VisionERA is capable of processing unstructured documents at massive scale and provide accuracy of upto 99% or more.
Without using NER as a technique for building this platform, it wouldn't have been possible for our platform to distinguish between different entries that mean the same. In fact, the capability to understand the context behind an individual entry is what makes VisionERA a standout document processing solution. It gives our platform to determine entries from multiple unstructured documents and source it under a single global variable that defines that data point. Adding to it, VisionERA offers exceptional auto correction capabilities, it is by virtue of NER in NLP that the platform is capable of doing it.
Named entity recognition has played a significant role in developing multiple varieties of applications we use on a daily basis. Starting from OTT platforms to music streaming services, and industry-based solutions such as VisionERA, without NER nothing would have come to reality. It is a great technique that has been helping developers to create a more customer-centric product. It allows an organization to provide tailor-made experience to its user without sitting and coding everything for the customer. It is truly a boon for organizations that are heavy on providing great customer service and customer experience. Adding to it, it is not far-fetched that in future each and every application might take benefit of this technique in some way or the other.
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