Named entity recognition (NER) in the form of Natural language processing (NLP) is one of the most data preprocessing tasks. But how can you use it?

As you know, Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment, and determine which parts are important. Named entity recognition (NER) in the form of NLP is one of the most data preprocessing tasks. It involves the identification of key information in the text and classification into a set of predefined categories. An entity is basically the thing that is consistently talked about or referred to in the text.

NER is the form of NLP.

At its core, NLP is just a two-step process, below are the two steps that are involved:

• Detecting the entities from the text
• Classifying them into different categories

Some of the categories that are the most important architecture in NER such that:

• Person
• Organization
• Place/ location

Other common tasks include classifying the following:

• date/time.
• expression
• Numeral measurement (money, percent, weight, etc)
• E-mail address

Deep Learning-Based NER:

Deep learning NER is much more accurate than the previous method, as it is capable to assemble words. This is due to the fact that it used a method called word embedding, which is capable of understanding the semantic and syntactic relationship between various words. It is also able to learn analyzes topic-specific as well as high-level words automatically. This makes deep learning NER applicable for performing multiple tasks. Deep learning can do most of the repetitive work itself, hence researchers for example can use their time more efficiently.