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Why natural language processing is important
What is Natural Processing ?
The natural language is the everyday language that is used by humans to communicate, such as English, German, or Korean. In contrast to machine languages, natural languages are hard to define with a specific set of rules. By combining the power of artificial intelligence, computational linguistics, and computer science, Natural Language Processing (Natural Language Processing) helps machines to understand the natural language. The ability of a computer program to understand human language as it is spoken and understood is Natural language processing.
A computer would deserve to be called intelligent if it could deceive a human into believing that it was human -Alan Turing
Read Also: How Conversational AI works ?
Why is Natural Language Processing required ?
The development of Natural Language Processing applications is challenging because computers traditionally work with precise, unambiguous, and highly structured languages called programming languages. The natural language is often obscure, and the linguistic structure depends on various complex variables, including slang, regional dialects, and social context.
With the ongoing growth of the World Wide Web and social media, there has been a drastic increase in online data. As the quantity of this data increases, the mechanisms to process this unstructured data and extract meaningful information from it becomes more challenging. With advanced Natural Language Processing techniques, we can overcome these inherent challenges and difficulties in the process.
What are the applications of Natural Language Processing ?
Natural language translations are the most widely used Natural Language Processing application. The need to overcome language barriers is more important and immediate than ever in this digitally connected world. The Natural Language Processing techniques help the machines to understand the meaning of sentences, identify the slang, and its emotion. Natural Language processing improves the efficiency of machine translation and are very useful for emotion analysis. It helps to identify the sentiment among several online posts and comments. The business firms make use of Natural Language Processing techniques to know about the customer’s opinions on their product and services from online reviews.
Natural Language Processing can perform Automatic summarization efficiently. Automatic summarization helps in summarizing the meaning of documents and information while keeping intact the emotional meanings of the information. Automatic summarization is of great value when applied to provide an overview of a news item or blog post while avoiding redundancy from multiple sources and maximizing the diversity of content obtained. Natural Language Processing can help to find the critical piece of information from a large knowledge base with greater efficiency and in less time.
The advanced Natural Language Processing techniques allow the non-programmers to interact with the computing systems and obtain useful information from it. Natural Language Processing helps users who are unfamiliar with the terminologies of the computing system by suggesting common synonyms to match with the original data. Spam filtering, language understanding, text classification, information extraction, question answering, Social website feeds, Voice recognition, and speech-to-text are the other typical applications of Natural Language Processing.
There are many open-source Natural Language Processing (Natural Language Processing) libraries, and the following are some of the popular ones:
Natural language toolkit (NLTK)
Gate Natural Language Processing library
Apache OpenNatural Language Processing.
Stanford Natural Language Processing suite
NLTK is written in Python and is the leading platform for building Natural Language Processing applications. It provides an intuitive framework along with substantial building blocks, consistent interfaces, and data structures.