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Ways to Use Natural Language Processing

What is NLP?

NLP is an abbreviation for Natural Language Processing. “Natural language“ is about human languages, such as English, Russian, Spanish, etc. “Processing” means that we teach computers to understand and generate human language by machine learning techniques.

Natural Language Processing can be defined as a branch of artificial intelligence that enables computers to understand human languages with the help of databases and synonym databases. NLP mainly deals with the linguistic patterns to fit and predict natural language patterns. On the other hand, data science deals with organizing a large amount of data, scientific methodologies, and mathematical methods to extract meaningful information from large databases, processing it intelligently and making the analysis possible with the help of various computer programs. The main aim of these two branches of science is to provide solutions for business and education purposes.

Autocorrect, Autocomplete, Language Translators, and Grammar Checkers

Autocorrect and grammar checkers are other popular NLP tools based on predictive text technology. This is very powerful software, here is how it works.

The user sends a text file of a document that they have just written out like an article, an audio recording, a picture or something else, and a question or some kind of challenge. This kind of text file is then run through a powerful NLP training system known as a statistical pattern recognition machine (or SVR for short).

The SVR is set up to search out all the anomalies that are in the document that you gave it, and it checks those anomalies against a set of rules that are specified in the client’s own language. Once the SVR has found what it deems as being a problem area in your document it notifies you. You will then have the opportunity to either edit the text file to fix the problem area, or you can save the entire document and take it to another tool that will allow you to fix the anomalies that were in the text file.

This whole technique can be used for many things, including guessing passwords, getting phone numbers or addresses from text files, predicting lots of random words or phrases, predicting lots of words or phrases from one text file, guessing words or phrases from a series of texts, and much more. You can see examples of it by using Grammarly(autocorrect and checking grammar mistakes), searching Google(autocomplete), Google Translator(translators). The list of the possible ways that you can use this software is endless.

Text Generating

NLP involves processes like Syntactic Structures, Language Acquisition, and Semantic Processes. These are just some of the many natural language generation applications, text classification in NLP, and article generation.

For those who would want to know more about the subject, text classification in NLP is the process of learning how to extract pieces of information from large amounts of text. In this case, the term “natural language processing” refers to the process of extracting data from natural conversations instead of from text documents using databases and programs. This type of text extraction is often referred to as “text mining.” An article is a piece of text; a text mining operation extracts “minutes of the conversation,” or all the words used in a particular conversation, to discover exciting facts or information.

Articleforge is a natural language processing tool that allows users to create articles, presentations, web pages, sales letters, and much more. It was taught on millions and millions of different text articles, just like humans, but in another way. We are learning to write texts through practice, implementing the argumentative analysis of a particular work of literature – https://studycrumb.com/literary-analysis-essay. But Articleforge’s studying process includes sequential models like LSTM.

One of the most impressive claims of Articleforge is that it is used in applications ranging from content generation for website ranking to search engine optimization and text mining.

Natural Language Processing in Marketing Analytics

Natural Language Processing in marketing analytics is a powerful tool to measure audience, response, and purchasing behavior in any retail or online setting. NLP helps companies understand customers’ preferences, tastes, and buying habits for generating and tracking various advertising, customer contact, and interactive marketing campaigns.

For instance, it can be used for:

  • the segmentation and identification of customers based on their past shopping behavior for targeted advertising;
  • topic exploration and extraction for creating relevant content;
  • sentiment analysis for identifying people’s responses to specific topics to improve SEO ranking;
  • to understand what customers search for, how often they search, and which keywords they use to find those items.

These tools provide businesses with detailed information on what they want from the products and services they offer, how they search for those products, and which keywords are frequently used to find those products. All of this allows companies to tailor their campaigns and track the return on investment (ROI) of their marketing campaigns.

NLP Text-Based Chatbots

NLP text-based chatbots are AI computer applications that are capable of natural conversation management. The application works on the principle of Natural Language Generation (NLG), which involves the generation of discourse from any given input, using specified word selection and grammatical construction. Natural conversations allow users to easily engage in real-time interactive sessions without being forced to go through the process of translation or interpretation.

NLP-powered agents are very similar to chatbots that work through translation and interpretation. However, they are used in non-linguistic applications and would therefore not allow the construction of texts from English or Spanish sources, etc. NLP chatbots are similar to web assistants that allow users to communicate through short messaging service (SMS). However, text-based chatbots can achieve success because they can understand and respond more effectively to queries based on natural conversations. For this reason, chatbots are now being used in a wide range of non-linguistic endeavors, including business, education, healthcare, retail, and even nonprofit organizations.

Voice Assistance: speech recognition and voice text messages

Apple and Google have announced voice recognition technologies that will allow natural language processing of spoken words, opening the door to entirely artificial intelligence technology. The best voice recognition software program is Google Assistant and Apple’s voice-recognition feature on the iPhone. Still, even these systems have a long way to go before replacing a live human assistant. The accuracy of speech recognition using NLP algorithms is still considered to be in the realm of science fiction.

The biggest issue with voice recognition for natural language processing is that while it can identify specific natural language phrases, it can not identify non-natural language expressions such as “You’re really frowning.” Google Assistant does a pretty good job at recognizing generally spoken phrases. However, it’s unable to identify specific, natural language dialogues.

The good news is that it doesn’t have to be this difficult. Many voice recognition programs include speech recognition filters that work on a natural language understanding of “how” a word is used rather than on a “what” knowledge. This allows voice assistance programs to turn “You’re really frowning” into “You’re upset” and much more. The result is a better understanding for your customer and ultimately increased satisfaction from voice-assisted interactions.

Email phishing detection

Spam email filtering is one of the latest techniques used by email systems to block spam. For years such filters have been labeled as “webroot spam blockers”, and have been used on large and small companies and organizations. Webroot is an email filtering company whose technologies include spam email classification, email forwarding, email delivery reporting, and spam blocking. While these are all good techniques, they are limited in that they only block email based on spam filters text files and not emails based on a keyword search or other text-based filtering techniques.

Spam email filters with natural language processing capabilities are far more flexible and provide better ways to prevent spam email than webroot filtering technology. NLP is simply the process of identifying natural language communication (in the context of a human conversation) and then re-phrasing that communication in a way that doesn’t sound spammy.  It allows filters to identify a particular phrase in an email and to pre-populate a user interface with a list of acceptable email messages based on the content of the message and the specific words in the phrase.

Many email filters with NLP technology will allow you to filter email based on a keyword search. If you input a known word or phrase into the text box, the email will be delivered to the inbox without the sender’s email address.  While spam email filters with NLP are not always 100% effective, they do have a high success rate when it comes to preventing spam emails from getting to your inbox.

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