Artificial intelligence (AI) is omnipresent and is changing the way we look at the world. However, the advent of AI and data analytics tools has led to the boom of data. And to process this mountain of raw data, we need Natural Language Processing. In technical parlance, NLP is a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, helps in creating and reading textual data, visual data, and more. Basically, it helps computers understand, interpret, and manipulate human language. From automatic translation or sentence completion to identify insurance fraud and powering chatbots, NLP is used almost everywhere. The main objective of NLP applications is to help humans have interaction with computers as they would with another human.
NLP consists of two basic divisions, viz., Natural Language Understanding and Natural Language Generation. Natural Language Understanding is the analytical branch of the Natural Language Processing. It is all about analyzing the contents of the text and understanding its insights. Meanwhile, Natural Language Generation is the operational branch of NLP, built for enhancing computer's capability to generate text, whether by converting data to written language, translating speech to written text, or converting text to audible speech.
Most of the NLP applications involve four necessary steps. These are sentence segmentation, word tokenization, part of speech or morph syntactic tagging, and syntactic or dependency parsing. However, certain challenges can hinder the functioning of NLP software. These are primarily due to irregularity and ambiguity of natural language. The language used by humans contains a multitude of words with numerous alternative uses, dialects, and more. While we are capable of expressing, perceiving, and interpreting very elaborate and nuanced meanings, machines need a clear set of well-defined algorithms to follow. The second challenge is the mutable nature of language structures, which therefore makes it complicated. Various phrase types can be formed out of the same bag of words. Hence, sometimes, one-time sentence boundary disambiguation can be difficult to achieve.
While the simple function of NLP is to facilitate understanding of human language, the following examples can highlight the versatility of this application.
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