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Introduction

Natural Language Processing is a component of artificial intelligence that deals with language and speech recognition, written text, interaction, and translation. Computers can process natural languages like English as well as communicate back to humans through combined voices or on-screen text content. NLP has a multiplicity of real-world applications in various fields, including medical research, Search Engines, and Business Intelligence. Especially in Business Intelligence domain software like Tableau, Salesforce, SAP Analytics to analysis data related to every departments of the business. Today NLP is booming thanks to the huge enhancement in access to data and the improvement in their computational power, which are allowing practitioners to reach meaningful results in areas like healthcare, media, finance, human resources, among others.

Why are organizations looking to invest? 

Artificial Intelligence has been in use for years, but only recently have companies viewed it as an investment. It can help bring down costs by removing redundancies and freeing up human resources for more strategic tasks, then there is a good expectation that AI is the answer. Companies are looking at investing in AI because of the intimidation and inaccuracy that’s circulated since its emergence into mainstream society. However,  The increasing demand of businesses is looking for an edge to keep up with current runners or ones coming from abroad,  When coupled with a reduced labor pool means AI is more important than ever before as we head closer towards it.

Additionally, with organizations relying on big data analytics more than ever before, having an AI/ML system in place to help recognize patterns in data will only get more important as time goes on. 

What are the benefits of using NLP in Life and Business?

There are three main benefits of using NLP in life and business:

• Excellence: Models excellence in every walk of life.

• Achievement: Helps you sharpen your skills and boost your achievements.

• Choice: Gives you more choice over what you do and the way you act.

• Efficiency: Automating the analysis of volumes of unstructured content in real-time.

• Speed: The value of information declines rapidly so insights need to be harvested swiftly.

• Consistency: A single model achieves consistency that is not achievable if performed by a number of human analysts, each of whom may interpret aspects of text slightly differently.

• Accuracy: Unstructured documents can be lengthy, and human analysts can potentially miss or misinterpret information.

Once NLP tools can know what a section of text is about and even measure things like sentiment, businesses can build up to prioritize and arrange their data in a way that suits their needs. All this business data contains a wealth of beneficial insights, and NLP can rapidly help businesses find what those insights are. It does this by helping machines make sense of human language in a faster, more accurate and more compatible way than human agents.

NLP tools process data in real-time, 24/7, and permit the same criteria to all your data, so you can make sure the results you receive are accurate and not riddled with inconsistencies. One of the main causes natural language processing is so critical to businesses is that it can be used to examine big volumes of text data, such as social media comments, customer support tickets, online reviews, news records.

Advantages of Natural Language Processing

•  The user doesn’t need to be trained in how to use the interface.

•  Suitable for physically handicapped people.

•  A sense of Personal Power.

•  Greater Social Confidence.

•  Freedom from Fears & Negative Thoughts

•  Better Health & Vitality.

Disadvantages of Natural Language Processing

• Reliability endures a matter- the interface can only respond to commands that have been programmed.

• Not widely serviceable as other forms of interface are often superior.

• Requires clarification dialogue.

• May require more keystrokes.

• May not show context.

How does Natural Language Processing work?

1. Data-Pre-Processing

Before NLP tools can create a sense of human language, data scientists will need to execute some fundamental NLP preprocessing tasks:

• Tokenization: breaks down chat into smaller semantic units or unique clauses.

• Part-of-speech-tagging: patching up some words as nouns, verbs, adjectives, adverbs, pronouns, etc.

• Stemming and lemmatization: Normalized words by decreasing them to their root forms.

• Stop word removal: screening out most common words that add little or no individual information, for example, prepositions and articles (at, to, a, the).

2. Natural Language Processing Algorithms

Once your data has been initialized, it’s time to proceed to the next step: building an NLP algorithm, and training it so it can elucidate natural language and perform particular tasks.

What is natural language processing used for?

Some of the main functions that natural language processing algorithms effects are:

• Text classification: It can also be useful for purpose detection, which helps forecast what the lecturer or creator may do based on the text they are producing. This involves allocating tags to content texts to put them in categories. This can be useful for point-of-view analysis, which helps the natural language processing algorithm resolve the sentiment, or feel behind a content.

• Text extraction: This involves automatically summarizing text and choosing the main pieces of data information. One example of this is keyword extraction, which pulls the most keywords from the text, which can be helpful for SEO. However, which reserved the names of people, places, entity recognition, and other entities from text. 

• Machine translation: This is the process by which a computer translates text from one language, like English, to added language, like French, without human intervention.

• Natural language generation: This involves using natural language processing algorithms to analyze unshaped data and automatically generate content based on that data.

Challenges of natural language processing

There are several challenges of natural language processing and most of them boil down to the reality that natural language is ever-evolving and every time somewhat ambiguous. 

Here, some points of challenges of NLP:

• Precision: Computers classically need humans to "speak" to them in a programming language that is exact, unambiguous and highly organized structured or through a bounded number of clearly pronounced voice commands.

• The tone of voice and inflection: NLP has not so far been improved. For instance, natural language processing does not raise sarcasm easily. The tone and inflection of speech may also vary between various modulations, which can be challenging for an algorithm to parse. These topics normally require understanding the words being used and their context in a talk with each other. 

• The evolving use of language: NLP is also challenged by the fact that language and the way people use it. Although there are language rules, none are written in stone, and they are subject to change over time. Hard data processing rules that work now may become out of date as the characteristics of real-world language change over time.

What are the techniques used in NLP?

Syntactic analysis and semantic analysis are the main procedures used to total Natural Language Processing tasks.

There is a description of how they can be used:

1. Syntax

Syntax refers to the order of words in a sentence such that they create grammatical sense. In NLP, syntactic analysis is used to evaluate how the natural language aligns with the grammatical rules.

Here are some syntax techniques that can be used:

• Lemmatization: It entails decreasing the different inflected forms of a word into a unique form for easy analysis.

• Morphological segmentation: It involves separating words into single units called morphemes.

• Word segmentation: It involves separating a big piece of continuous text into distinct units.

• Part-of-speech tagging: It involves recognizing the part of speech for every word.

• Parsing: It involves effort grammatical analysis for the provided sentence.

• Sentence breaking: It involves placing sentence boundaries on a huge piece of text.

• Stemming: It involves splitting the inflected words to their root form.

2. Semantics

Semantic analysis is one of the hard aspects of Natural Language Processing that has not been fully determined yet. Semantics refers to the meaning that is sent by content. It involves carrying out computer algorithms to know the meaning and interpretation of words and how sentences are structured.

Here are some techniques in semantic analysis:

• Named entity recognition: It involves determining the parts of a text that can be specified and grouped into preset groups. Examples of such categories include names of people and names of places.

• Word sense disambiguation: It involves offering to mean a word based on the context.

• Natural language generation: It involves using databases to derive semantic intentions and transfer them into human language.

  

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