Natural Language Processing

- Overview 

Natural language processing is the ability of "intelligent" computer systems to understand human language (written and spoken). This is often called natural language. 

Natural language processing (NLP) is a subfield of artificial intelligence (AI). It helps machines process and understand human language so that they can automate repetitive tasks. Examples include machine translation, summarization, ticket classification, and spell checking.

Natural language processing has been around for more than fifty years, with the technology originating in linguistics, or the study of human language. It has various practical applications in many industries and fields, including intelligent search engines, advanced medical research, and business processing intelligence. 

Take sentiment analysis, for example, which uses natural language processing to detect emotions in text. This classification task is one of the most popular tasks in NLP and is often used by companies to automatically detect brand sentiment on social media. Analyzing these interactions can help brands identify urgent customer issues that require an immediate response, or monitor overall customer satisfaction.

Natural language processing is not only concerned with processing, as recent developments in the field such as the introduction of Large Language Models (LLMs) and GPT3, are also aimed at language generation as well.

- Why is NLP Important?

One of the main reasons why NLP is so important to businesses is that it can be used to analyze large amounts of textual data, such as social media comments, customer support tickets, online reviews, news reports, and more.

All this business data contains a wealth of valuable insights that NLP can quickly help businesses uncover. It does this by helping machines understand human language faster, more accurately, and more consistently than human agents. 

NLP tools process data on the fly 24/7 and apply the same criteria to all data, so you can be sure that the results you receive are accurate and not riddled with inconsistencies.

Once NLP tools can understand the meaning of a piece of text and even measure things like sentiment, businesses can start to prioritize and organize material in a way that suits their needs.  

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- Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of AI that helps computers understand, interpret and manipulate human language. NLP helps computers communicate with humans in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.

NLP is concerned with the practical issues of using computer systems to process human language. Statistical NLP consists of applying machine learning and statistical techniques to produce inferences providing the ability to reason and make decisions over text much like humans do.  

NLP systems understand written and spoken language; possibilities include automatic translation of text from one language to another, or understanding text on Wikipedia to produce knowledge about the world. Machine listening systems understand audio signals, with applications like listening for crashes at traffic lights, or transcribing polyphonic music automatically. 

- Natural Language Generation (NLG) and Natural Language Understanding (NLU)

Natural language understanding (NLU) is the ability of a computer to understand the meaning of written or spoken language. NLU uses syntactic and semantic analysis to determine the intent of the language. NLU is a subset of natural language processing (NLP). 

Natural language generation (NLG) is the process of creating natural language text or speech based on a given data set. NLG is a field of AI that focuses on generating natural language output. 

In general terms, NLG and NLU are subsections of a more general NLP domain that encompasses all software which interprets or produces human language, in either spoken or written form:

• NLU takes up the understanding of the data based on grammar, the context in which it was said, and decide on intent and entities.
• NLP converts a text into structured data.
• NLG generates text based on structured data.

- Computational Linguistics

Computational linguistics is the scientific study of language from a computational perspective. Computational linguists are interested in providing computational models of various kinds of linguistic phenomena. These models may be "knowledge-based" ("hand-crafted") or "data-driven" ("statistical" or "empirical"). 

Work in computational linguistics is in some cases motivated from a scientific perspective in that one is trying to provide a computational explanation for a particular linguistic or psycholinguistic phenomenon; and in other cases the motivation may be more purely technological in that one wants to provide a working component of a speech or natural language system. 

Indeed, the work of computational linguists is incorporated into many working systems today, including speech recognition systems, text-to-speech synthesizers, automated voice response systems, web search engines, text editors, language instruction materials, to name just a few. 

Computational linguists develop computer systems that deal with human language. They need a good understanding of both programming and linguistics. This is a challenging and technical field, but skilled computational linguists are in demand and highly paid. Following are the areas a computational linguist should concentrate on: programming skills, math and statistics, linguistics, natural language processing. 

[More to come ...]