Saturday, January 19, 2019

What Is Neuro-Linguistic Programming (NLP)?

NLP - Neuro-Linguistic Programming

“Hey, Siri” “Alexa” “Ok Google” Whenever one will speak these words in their phones with an internet connection, you will see something happens which is one of the coolest wonders of the modern world. Now, are you a YouTube user? If yes, have you seen one thing which occurred during the month of May-June, 2018? If you are unable to remember this, then I am presenting this video which will completely refresh your day to see the NEXT AMAZING thing happening around the World and Google is that cause.:

So, who is sitting in the driving seat to make it work like that? How he/she is changing the gears in order to run and drive the users to get surprises? Why is this happening that a machine can answer one’s question in such a compatible, charismatic manner? The answer is NLP (Neuro-Linguistic Programming). In simple words, NLP stands for Neuro-Linguistic Programming. Neuro refers to your neurology; Linguistic refers to language; programming refers to how that neural language functions. In other words, learning NLP is like learning the language of your own mind!
  • Neuro-Linguistic Programming (NLP) is like a user’s manual for the brain, and taking NLP training is like learning how to become fluent in the language of your mind so that the ever-so-helpful “server” that is your unconscious will finally understand what you actually want out of life. 
  • NLP is the study of excellent communication–both with yourself and with others. It was developed by modeling excellent communicators and therapists who got results with their clients. NLP is a set of tools and techniques, but it is so much more than that. It is an attitude and a methodology of knowing how to achieve your goals and get results. 
Once this term gets elaborated, we call this Natural Language Processing. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. 

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How NLP started? 

Karl Marx had well stated once,” History repeats itself, first as tragedy, second as farce.” This line creates an immense impact in terms of understanding how this NLP evolved over the sands of time.  So, let’s check the timeline: 
  • 1970: Dr. Bandler (Student at the University of California in IT) and Dr. John Grinder (lecturer in linguistics at the same university) worked together to co-model the contemporary “wizards” of personal growth such as Fritz Perls (Gestalt Therapist), Virginia Satir (Family Therapist) and Dr. Milton Erickson (Hypnotist).  Combining their individual expertise, Bandler and Grinder were able to identify and map patterns of behavior. 
  • During their collaborative time, Richard and John co-wrote several books, including Structure of Magic Vol I & II, Changing with Families and The Patterns of The Hypnotic Techniques of Milton H Erickson. These books, based on the language patterns and behaviors of their contemporary personal development experts, were the forerunners of what was to become the model of Neuro-linguistic Programming. 
  • NLP was created as a modeling tool and not as a type of therapy. 
  • The co-developers of NLP, Richard Bandler, and Dr. John Grinder, worked with a group of students who attended early experimental workshops where the basic and first behavioral patterns and techniques of NLP were tried out.  
  • 1972: The group started in 1972 and met periodically for over four years. This core group of individuals was the first to assist Richard and John to spread the patterns and techniques of NLP via small and large group conferences and training workshops throughout the United States and Canada. They were the first practitioners of NLP. 

How this language gets processed? 

Once you have identified, extracted, and cleansed the content needed for your use case, the next step is to have an understanding of that content. In many use cases, the content with the most important information is written down in a natural language (such as English, German, Spanish, Chinese, etc.) and not conveniently tagged. To extract information from this content you will need to rely on some levels of text mining, text extraction, or possibly full-up natural language processing (NLP) techniques. 
Typical full-text extraction for Internet content includes: 
  • Extracting entities – such as companies, people, dollar amounts, key initiatives, etc. 
  • Categorizing content – positive or negative (e.g. sentiment analysis), by function, intention or purpose, or by industry or other categories for analytics and trending 
  • Clustering content – to identify main topics of discourse and/or to discover new topics 
  • Fact extraction – to fill databases with structured information for analysis, visualization, trending, or alerts 
  • Relationship extraction – to fill out graph databases to explore real-world relationships. 
The input to natural language processing will be a simple stream of Unicode characters (typically UTF-8). Basic processing will be required to convert this character stream into a sequence of lexical items (words, phrases, and syntactic markers) which can then be used to better understand the content. 

The basics include: 
  • Structure extraction – identifying fields and blocks of content based on tagging 
  • Identify and mark sentence, phrase, and paragraph boundaries – these markers are important when doing entity extraction and NLP since they serve as useful breaks within which analysis occurs.  

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Open source possibilities include the Lucene Segmenting Tokenizer and the Open NLP sentence and paragraph boundary detectors. 
  • Language identification – will detect the human language for the entire document and for each paragraph or sentence. Language detectors are critical to determining what linguistic algorithms and dictionaries to apply to the text. 
  • Open source possibilities include Google Language Detector or the Optimize Language Detector or the Chromium Compact Language Detector 
  • API methods include Bing Language Detection APIIBM Watson Language Identification, and Google Translation API for Language Detection 
  • Tokenization – to divide up character streams into tokens which can be used for further processing and understanding. Tokens can be words, numbers, identifiers or punctuation (depending on the use case) 
  • Open source tokenizers include the Lucene analyzers and the Open NLP Tokenizer. 
  • Basis Technology offers a fully featured language identification and text analytics package (called Rosette Base Linguistics) which is often a good first step to any language processing software. It contains language identification, tokenization, sentence detection, lemmatization, decompounding, and noun phrase extraction. 
  • Search Technologies has many of these tools available, for English and some other languages, as part of our Natural Language Processing toolkit. Our NLP tools include tokenization, acronym normalization, lemmatization (English), sentence and phrase boundaries, entity extraction (all types but not statistical), and statistical phrase extraction. These tools can be used in conjunction with the Basis Technology’ solutions. 
  • Acronym normalization and tagging – acronyms can be specified as “I.B.M.” or “IBM” so these should be tagged and normalized. 
  • Lemmatization / Stemming – reduces word variations to simpler forms that may help increase the coverage of NLP utilities. 
Lemmatization uses a language dictionary to perform an accurate reduction to root words. Lemmatization is strongly preferred to stemming if available. Search Technologies has lemmatization for English and our partner, Basis Technologies, has lemmatization for 60 languages. 
Stemming uses simple pattern matching to simply strip suffixes of tokens (e.g. remove “s”, remove “ing”, etc.). The Open Source Lucene analyzers provide stemming for many languages. 
  • Decompounding – for some languages (typically Germanic, Scandinavian, and Cyrillic languages), compound words will need to be split into smaller parts to allow for accurate NLP. 
For example “Samstag morgen” is “Saturday Morning” in German 
Entity extraction – identifying and extracting entities (people, places, companies, etc.) is a necessary step to simplify downstream processing. There are several different methods: 

Regex extraction – good for phone numbers, ID numbers (e.g. SSN, driver’s licenses, etc.), e-mail addresses, numbers, URLs, hashtags, credit card numbers, and similar entities. 

Dictionary extraction – uses a dictionary of token sequences and identifies when those sequences occur in the text. This is good for known entities, such as colors, units, sizes, employees, business groups, drug names, products, brands, and so on. 

Complex pattern-based extraction – good for people names (made of known components), business names (made of known components) and context-based extraction scenarios (e.g. extract an item based on its context) which are fairly regular in nature and when high precision is preferred over high recall. 

Statistical extraction – use statistical analysis to do context extraction. This is good for people names, company names, geographic entities which are not previously known and inside of well-structured text (e.g. academic or journalistic text). Statistical extraction tends to be used when a high recall is preferred over high precision. 
  • Phrase extraction – extracts sequences of tokens (phrases) that have a strong meaning which is independent of the words when treated separately. These sequences should be treated as a single unit when doing NLP. For example, “Big Data” has a strong meaning which is independent of the words “big” and “data” when used separately. All companies have these sorts of phrases which are in common usage throughout the organization and are better treated as a unit rather than separately. Techniques to extract phrases include: 
Part of speech tagging – identifies phrases from a noun or verb clauses 
Statistical phrase extraction - identifies token sequences which occur more frequently than expected by chance 
Hybrid - uses both techniques together and tends to be the most accurate method. 

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Applications of NLP: 
We have covered this NLP but what about learning if it hasn’t been applied anywhere. It will act like trash. So we should quickly look at the things which matter the most and bring a sense of concern in us. First of all, one should that it will bring enhancement to the answering mechanism. 
  • Chatbot: Chatbots are one of the most fascinating system creatures developed in line with the NLP. You get quick answers sometimes for the questions raised through these bots. Understanding the concerned topic, they answer accordingly from the database. 
  • Summarization/Sentimental Analysis: Information overload is a real phenomenon in our digital age, and already our access to knowledge and information far exceeds our capacity to understand it. Another desired outcome is to understand deeper emotional meanings, for example, based on aggregated data from social media, can a company determine the general sentiment for its latest product offering? This branch of NLP will become increasingly useful as a valuable marketing asset. 
  • Spam CombatSpam filters have become important as the first line of defense against the ever-increasing problem of unwanted email. But almost everyone that uses email extensively has experienced agony over unwanted emails that are still received, or important emails that have been accidentally caught in the filter. The false-positive and false-negative issues of spam filters are at the heart of NLP technology, again boiling down to the challenge of extracting meaning from strings of text. A technology that has received much attention is Bayesian spam filtering, a statistical technique in which the incidence of words in an email is measured against its typical occurrence in a corpus of spam and non-spam emails. 
  • Translation: As the world's information is online, the task of making that data the becomes increasingly important. The challenge of making the world's information accessible to everyone, across language barriers, has simply outgrown the capacity for human translation. But machine translation offers an even more scalable alternative to harmonizing the world's information. 
  • Managing the Advertisement funnel: Reaching out to the right patron of your product is the ultimate goal for any business. NLP matches the right keywords in the text and helps to hit the right customers. Keyword matching is the simple task of NLP yet highly remunerative for businesses. 
The system behind the NLP concept is statistical in nature. For this concept to move from (NLP) to (NLU) where the consumer can get to see and experience a human emotional connect with the machines, is the future prospect to work towards. Over the last decade, the information technology industry has taken its leap of faith and dug deep into the various aspects of Natural Language Processing. 

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