Named entity recognition is a component of natural language processing. This is also called entity attraction. This will help identify predefined categories of objects in a body of text. These categories can include names of organizations or individuals, locations, quantities, percentages, monetary values, and medical codes.
It is a process where a tax amount is taken and identifies the entities that refer to each category. It hails in processing large amounts of unstructured text and extracting crucial information. Now-named entity recognition also uses deep learning and machine learning techniques to identify data.
What are the Various Techniques of Named Entity Recognition?
Any organization that uses NER for its unstructured data extraction bill relies on various techniques. However, there are three broad categories in NER. Let us briefly understand them.
Rule-Based Approach: This technique will involve creating a set of rules for the grammar of a language. These rules are data used to identify entities in a text based on the structure and grammatical features. This process can be time-consuming and does not entirely generalize the unseen data.
Machine Learning Approach: This technique involves training an artificial intelligence-driven machine learning model on a data set that uses algorithms like maximum entropy, a complex statistical language model. This technique can vary from essential machine learning to more complex deep learning approaches. This process generalizes better to unseen data but may require a large amount of labeled training data, which is expensive.
Hybrid Approach: This process combines rule-based and machine learning approach to optimize the strength of both. The rule-based system will quickly identify the entities. And also, the machine learning system will help identify more complex entities.
In conclusion, named entity recognition has made massive progress in languages like English. It will also help identify complex terms that can be used in medical fields for complex diseases.
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