A Coding Scheme That Translates Real-world Data Into a Form That Computers Can Process Easily.

• IBM Cloud Learn Hub
• What is Tongue Processing?

• What is natural language processing?
• NLP tasks
• NLP tools and approaches
• NLP use cases
• Natural language processing and IBM Watson

Natural Language Processing (NLP)

Tongue processing strives to build machines that understand and respond to text or vocalization data—and respond with text or spoken language of their own—in much the same way humans exercise.

What is natural language processing?

Natural language processing (NLP) refers to the co-operative of computer science—and more specifically, the co-operative of artificial intelligence or AI—concerned with giving computers the power to understand text and spoken words in much the same way human beings can.

NLP combines computational linguistics—rule-based modeling of homo language—with statistical, car learning, and deep learning models. Together, these technologies enable computers to process man language in the grade of text or voice information and to 'understand' its full significant, complete with the speaker or writer's intent and sentiment.

NLP drives computer programs that interpret text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There's a proficient chance y'all've interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. Only NLP likewise plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

NLP tasks

Human language is filled with ambiguities that brand information technology incredibly hard to write software that accurately determines the intended meaning of text or vocalisation information. Homonyms, homophones, sarcasm, idioms, metaphors, grammer and usage exceptions, variations in sentence structure—these simply a few of the irregularities of human language that have humans years to learn, simply that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to exist useful.

Several NLP tasks interruption downwardly human text and voice data in means that help the computer brand sense of what it's ingesting. Some of these tasks include the following:

• Speech recognition, also called speech-to-text, is the job of reliably converting voice data into text data. Speech recognition is required for whatever awarding that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the mode people talk—apace, slurring words together, with varying emphasis and intonation, in different accents, and ofttimes using incorrect grammer.
• Part of spoken communication tagging, also chosen grammatical tagging, is the procedure of determining the lexical category of a particular discussion or piece of text based on its use and context. Lexical category identifies 'make' equally a verb in 'I tin can make a paper aeroplane,' and as a noun in 'What make of auto do you lot ain?'
• Word sense disambiguation is the pick of the meaning of a give-and-take with multiple meanings  through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in 'brand the grade' (accomplish) vs. 'brand a bet' (identify).
• Named entity recognition,or NEM, identifies words or phrases as useful entities. NEM identifies 'Kentucky' as a location or 'Fred' equally a man'south name.
• Co-reference resolution is the task of identifying if and when ii words refer to the aforementioned entity. The most common case is determining the person or object to which a certain pronoun refers (e.thousand., 'she' = 'Mary'),  but information technology can also involve identifying a metaphor or an idiom in the text  (e.g., an instance in which 'bear' isn't an animal but a big hairy person).
• Sentiment analysisattempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
• Natural language generationis sometimes described equally the opposite of speech recognition or spoken communication-to-text; it'south the task of putting structured information into human linguistic communication.

See the weblog post "NLP vs. NLU vs. NLG: the differences between three natural language processing concepts" for a deeper await into how these concepts relate.

NLP tools and approaches

Python and the Tongue Toolkit (NLTK)

The Python programing linguistic communication provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are establish in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.

The NLTK includes libraries for many of the NLP tasks listed above, plus libraries for subtasks, such as judgement parsing, give-and-take segmentation, stemming and lemmatization (methods of trimming words downwardly to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the reckoner ameliorate understand the text). It as well includes libraries for implementing capabilities such every bit semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.

Statistical NLP, automobile learning, and deep learning

The earliest NLP applications were mitt-coded, rules-based systems that could perform certain NLP tasks, simply couldn't hands scale to adjust a seemingly endless stream of exceptions or the increasing volumes of text and voice information.

Enter statistical NLP, which combines reckoner algorithms with motorcar learning and deep learning models to automatically extract, classify, and label elements of text and vocalism data and then assign a statistical likelihood to each possible meaning of those elements. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enable NLP systems that 'learn' every bit they work and extract e'er more authentic meaning from huge volumes of raw, unstructured, and unlabeled text and vocalisation information sets.

For a deeper dive into the nuances between these technologies and their learning approaches, see "AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What'south the Difference?"

NLP apply cases

Tongue processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:

• Spam detection:Y'all may not call back of spam detection as an NLP solution, merely the best spam detection technologies use NLP's text classification capabilities to browse emails for language that often indicates spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency, misspelled company names, and more. Spam detection is 1 of a handful of NLP bug that experts consider 'mostly solved' (although you may argue that this doesn't match your e-mail experience).
• Machine translation:Google Interpret is an instance of widely available NLP engineering science at piece of work. Truly useful machine translation involves more than than replacing words in ane linguistic communication with words of another.  Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired touch in the output language. Machine translation tools are making good progress in terms of accurateness. A swell way to examination whatever machine translation tool is to translate text to 1 language and then back to the original. An oft-cited classic instance: Not long ago, translating "The spirit is willing but the flesh is weak" from English language to Russian and back yielded "The vodka is skilful but the meat is rotten." Today, the event is "The spirit desires, but the flesh is weak," which isn't perfect, but inspires much more conviction in the English-to-Russian translation.
• Virtual agents and chatbots: Virtual agents such equally Apple's Siri and Amazon'south Alexa use speech recognition to recognize patterns in vocalism commands and tongue generation to respond with appropriate activity or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even amend responses or options over time. The adjacent enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
• Social media sentiment analysis: NLP has go an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more than to excerpt attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
• Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or decorated readers who don't have fourth dimension to read full text. The best text summarization applications use semantic reasoning and natural linguistic communication generation (NLG) to add useful context and conclusions to summaries.

Tongue processing and IBM Watson

• IBM has innovated in the artificial intelligence space by pioneering NLP-driven tools and services that enable organizations to automate their complex business processes while gaining essential business insights. These tools include:
  • Watson Discovery - Surface high-quality answers and rich insights from your complex enterprise documents - tables, PDFs, big data and more - with AI search. Enable your employees to make more than informed decisions and save fourth dimension with existent-fourth dimension search engine and text mining capabilities that perform text extraction and analyze relationships and patterns cached in unstructured data. Watson Discovery leverages custom NLP models and automobile learning methods to provide users with AI that understands the unique language of their industry and business organization. Explore Watson Discovery
  • Watson Tongue Agreement (NLU) - Clarify text in unstructured data formats including HTML, webpages, social media, and more. Increase your understanding of human being language by leveraging this natural linguistic communication tool kit to identify concepts, keywords, categories, semantics, and emotions, and to perform text nomenclature, entity extraction, named entity recognition (NER), sentiment assay, and summarization. Explore Watson Natural Language Understanding
  • Watson Assistant - Better the client experience while reducing costs. Watson Assistant is an AI chatbot with an like shooting fish in a barrel-to-utilise visual builder so you can deploy virtual agents across whatsoever channel, in minutes.  Explore Watson Assistant

  • Purpose-built for healthcare and life sciences domains, IBM Watson Analyst for Clinical Data extracts key clinical concepts from tongue text, like atmospheric condition, medications, allergies and procedures. Deep contextual insights and values for key clinical attributes develop more meaningful information. Potential data sources include clinical notes, discharge summaries, clinical trial protocols and literature data.

• For more information on how to get started with one of IBM Watson's tongue processing technologies, visit the IBM Watson Natural Language Processing folio.

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Source: https://www.ibm.com/cloud/learn/natural-language-processing

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