What is Natural Language Processing?

Using NLP driver text analytics to monitor viewer reaction on social media helps a production company to see how storylines and characters are being received. For example, social media site Twitter is often deluged with posts discussing TV programs. Sentiment analysis helps to determine the attitude and intent of the writer. Every time that Alexa or Siri responds incorrectly it uses the data derived from its response to improve and respond correctly the next time the question is asked. Automation also enables company employees to focus on more high-value tasks.

This requires an application to be intelligent enough to separate paragraphs or walls of text into appropriate sentence units. However, like many technologies, proper implementation faces a number of challenges. This application also helps chatbots and virtual assistants communicate and improve. Over 70 years ago programmers used punch cards to communicate with their machines. Humans use either spoken or written language to communicate with each other.

Examples of Natural Language Processing (NLP)

Natural language processing is also driving Question-Answering systems, as seen in Siri and Google. As the amount of online information continues to grow, the ability to easily access information in a foreign language grows in importance. Natural language processing is also helpful in analysing large data streams, quickly and efficiently. Natural language processing (NLP) is an increasingly becoming important technology.

Here the speaker just initiates the process doesn’t take part in the language generation. It stores the history, structures the content that is potentially relevant and deploys a representation of what it knows. All these forms the situation, while selecting subset of propositions that speaker has.

Advantages of NLP

Natural Language Processing can be applied into various areas like Machine Translation, Email Spam detection, Information Extraction, Summarization, Question Answering etc. Next, we discuss some of the areas with the relevant work done in those directions. To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation. While Natural Language Processing has its limitations, it still offers huge and wide-ranging benefits to any business.

Various researchers (Sha and Pereira, 2003; McDonald et al., 2005; Sun et al., 2008) [83, 122, 130] used CoNLL test data for chunking and used features composed of words, POS tags, and tags. So, for building NLP systems, it’s important to include all of a word’s possible meanings and all possible synonyms. Text analysis models may still occasionally make mistakes, but the more relevant training data they receive, the better they will be able to understand synonyms. Also, NLP has support from NLU, which aims at breaking down the words and sentences from a contextual point of view. Finally, there is NLG to help machines respond by generating their own version of human language for two-way communication.

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Some phrases and questions actually have multiple intentions, so your NLP system can’t oversimplify the situation by interpreting only one of those intentions. For example, a user may prompt your chatbot with something like, “I need to cancel my previous order and update my card on file.” Your AI needs to be able to distinguish these intentions separately. An HMM is a system where a shifting takes place between several states, generating feasible output symbols with each switch. The sets of viable states and unique symbols may be large, but finite and known.

Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges.

This model is called multi-nomial model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document. Most text categorization approaches to anti-spam Email filtering have used multi variate Bernoulli model (Androutsopoulos et al., 2000) [5] [15]. There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc.

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