What Is Statistical Natural Language Processing?

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Statistical NLP aims to do statistical inference for the field of natural language. Statistical inference in general consists of taking some data (generated in accordance with some unknown probability distribution) and then making some inference about this distribution.22 Sept 2017

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NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.

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Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.

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NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference.

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Or is NLP hard to learn? NLP is easy to learn if you have a touch of curiosity, courage, ambition, discipline and openness. Let's assume you're learning NLP to be effective using it on yourself, with your colleagues and your clients.

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How does Alexa work? According to Adi Agashe, Program Manager at Microsoft, Alexa is built based on natural language processing (NLP), a procedure of converting speech into words, sounds, and ideas.

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Natural Language Processing, or NLP, is how computers can understand human languages. For example, when you speak to voice-activated virtual assistants like Alexa or Siri, they listen, understand your speech, and perform an action based on what you've said.

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Amazon Lex is the natural language processing (NLP) service from AWS that powers conversational AI solutions for voice and chat.

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LUIS is free for Testing and Educational use. It is about 10000 conversations per month. However in production environment it has to be paid. The important this is you can access LUIS feature via Azure or can independently integrate it into your application.

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We connect to it via website search bars, virtual assistants like Alexa, or Siri on our smartphone. The email spam box or voicemail transcripts on our phone, even Google Translate, all are examples of NLP technology in action.

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Available Cloud NLP Engines: Dialogue flow, Amazon Comprehend, Microsoft LUIS and Google's Cloud AutoML.

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Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools.

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As we mentioned earlier, Deep Learning and NLP are both parts of a larger field of study, Artificial Intelligence. While NLP is redefining how machines understand human language and behavior, Deep Learning is further enriching the applications of NLP.

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“NLP makes it possible for humans to talk to machines:” This branch of AI enables computers to understand, interpret, and manipulate human language. Like machine learning or deep learning, NLP is a subset of AI.

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Formally, Natural Language Processing or NLP is defined as the application of computational techniques for the analysis and the synthesis of text. The aim of NLP is to give computers the ability to do tasks involving human language.

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The five phases of NLP involve lexical (structure) analysis, parsing, semantic analysis, discourse integration, and pragmatic analysis.