Natural language processing (NLP) is the capacity of a computer program to understand human language as it is spoken and written which is referred to as natural language. It is a factor of artificial intelligence (AI).
Natural language processing has prevailed for more than 50 years and has roots in the arena of linguistics. It has a mixture of real-world applications in several fields, comprising medical research, search engines, and business intelligence.
The following explanations will give you the answer to the question “what is natural language processing?”.
NLP facilitates computers to interpret natural language as humans do. Whether the language is verbal or written, natural language processing utilizes artificial intelligence to take real-world input, function it and convert it into a form in which the computer can understand.
As humans have various sensors computers have programs to read and microphones to collect audio. And just as humans have a brain to cleanse that input, computers have a program to process their respective inputs. At some point in processing, the input is converted to code that the computer can understand.
There are many distinct natural language processing algorithms, but two main types are commonly used:
Rules-based system: Cautiously designed linguistic principles are used by this system. This method was used early on in the growth of natural language development and is still used.
Machine Learning-based system: Machine learning algorithms use statistical methods. They learn to perform tasks founded on the training data they are provided and adjust their methods as more data is filtered.
The importance of Natural Language Processing
If a user depends on natural language processing for search, the program will understand that cloud computing is an entity, that cloud is a defined form of cloud computing, and that SLA is an industry acronym for service-level agreement.
NLP can be used to interpret free, unstructured text and make it analyzable. There is an enormous amount of information on patient medical records. Before deep learning-based NLP models, this information was inapproachable to computer-assisted analysis and could not be analyzed in any systematic way. With NLP analysts can analyze through massive amounts of free text to find relevant information.
Sentiment analysis is another major use case for NLP. Using sentiment analysis, data scientists can assess comments on social media to see how their business’s brand is executing, or review notes from customer service teams to identify areas where people want the business to perform
Even though there is denial, natural language processing is making significant strides in the medical imaging field.
Approaches of Natural Language Processing
Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to enhance a program’s understanding.
Deep learning models expect massive amounts of labeled data for the natural language processing algorithm to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to natural language processing.
Earlier approaches to natural language processing implicated a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in-text and given specific responses when those phrases appeared.
Although the possibility looks extremely demanding and full of difficulties for NLP, the domain is developing at a very fast pace and we are likely to reach a level of development in the coming years that will make complex applications look feasible.
