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The Fundamentals of Natural Language Processing and Natural Language Generation

By   /  August 2, 2018  /  No Comments

NLP and NLGNatural Language Processing (NLP) and Natural Language Generation (NLG) have gained importance in the field of Machine Learning (ML) due to the critical need to understand text, with its varying structure, implied meanings, sentiments, and intent. Natural Language Processing and Natural Language Generation have removed many of the communication barriers between humans and computers by translating machine language into human language, and by creating opportunities for humans to accomplish tasks that were impossible before.

Often in use for fraud detection and security applications, NLG and NLP jointly enable automated assistants and tools to uncover meanings from raw data. There are some technology barriers that stand in the way of full adoption of NLP and NLG, but once these hurdles are crossed, it’s anticipated that AI applications will drive customer applications, especially those that deal with heavy-duty Text Analytics.

According to Gartner, “By 2019, natural-language generation will be a standard feature of 90 percent of modern BI and Analytics platforms.” Top 10 Hot Artificial Intelligence (AI) Technologies summarizes a 2017 Forrester survey of technology in use or anticipated to be, including NLG and NLP.

What is Natural Language Processing?

Artificial Intelligence: Natural Language Processing Fundamentals describes NLP as the “process of producing meaningful phrases and sentences in the form of natural language.” Natural Language Processing precludes Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU takes the data input and maps it into natural language. NLG conducts information extraction and retrieval, sentiment analysis, and more.

The five phases of NLP involve lexical (structure) analysis, parsing, semantic analysis, discourse integration, and pragmatic analysis. Some well-known application areas of NLP are Optical Character Recognition (OCR), Speech Recognition, Machine Translation, and Chatbots.

In Natural Language Processing, the Machine Learning training algorithms study millions of examples of text — words, sentences, and paragraphs — written by humans. By studying the samples, the training algorithms gain an understanding of the “context” of human speech, writing, and other modes of communication. This training helps NLP software to differentiate between meanings of various texts.

In terms of processing sequence, NLG precedes NLP. NLG, a subset of Artificial Intelligence, converts data into natural sounding text — the way it is spoken or written by a human. In everyday life, you probably come across many instances of NLG without realizing it. When you ask Alexi for a forecast or Siri for directions, NLG is at work behind the scenes. NLG helps companies like Narrative Science or Automated Insights deliver data storytelling at scale.

Once NLP unlocks the context hidden in data and converts it into human language, NLP takes the output and analyses the text in context. You can think of NLG and NLP engaged in a joint endeavor to provide readymade conversational interfaces on top of many different AI applications. Natural language generation and processing are rapidly gaining ground across application areas, and Alexa is just one example of their worldwide success.

The Myth Surrounding Natural Language Generation  

Natural Language Generation is the technology that analyzes, interprets, and organizes data into comprehensible, written text. NLG aids the machine in sorting through many variables and putting “text into context,” thus delivering natural-sounding sentences and paragraphs that observe the rules of English grammar. In this context, you may find the KDNugget post titled Natural Language Generation overview – is NLG is worth a thousand pictures? quite enlightening.

With NLG, Data Scientists are free to dive directly into Data Analysis without worrying about intricate data preparation methods. The well-known NLG vendors in the market today include Arria, Narrative Science, and BeyondCore, which was recently acquired by Salesforce. According to AI, Machine Learning, NLP, and NLG: Your Basic Guide to Artificial Intelligence in Business, NLG vendors are increasingly tying up with BI solution providers to offer powerful solutions. This embedded-NLP capability of latest BI platforms is described by Matt Rauscher, Vice President of Yseop:

“Savvy takes data from a CRM application, and its rules engine automatically decides, based on the data, what products a salesperson should sell to which customers, and then the NLG tool writes what they need to do and why.”

The Market Success Story of Natural Language Processing   

Lately, prominent market-watchers like IDC, Forrester, and Gartner have offered their insights and expert views on the commercial viability of Natural Language Processing in multiple market reports. The Commercial NLP Landscape in 2017 encapsulates the most significant findings of those market reports, and offers convincing arguments in support of the technical functionality of conversational interfaces that have already gained market clout.

The crucial part of this article is an in-depth analysis of “chatbots,” which are fighting for existence in the presence of sophisticated smart phones. Additionally, the article reviews common text-analytics features such as entity recognition, concept extraction, text classification, sentiment analysis, and relation extraction or parsing.

Text Analytics is such a hot topic that the major IT vendors have started offering their own Text Analytics solutions. For example, IBM now offers SPSS Text Analytics, SAS offers Text Miner software, SAP has launched HANA Text Analytics, and Oracle has bundled text mining features in its Data Miner. This trend indicates that stand-alone Text Analytics vendors may soon find it difficult to market their solutions with so many major larger IT players offering bundled solutions.

The Commercial NLP Landscape hints that “sentiment analysis” is probably the main focus of Text Analytics technologies today, which has propelled vendors to redefine their solutions as social CRM or CEM offering.

Is Natural Language Processing a Form of Machine Linguistics?

A Guide to NLP: A Confluence Of AI And Linguistics compares Natural Language Processing to the field of Linguistics, and suggests that NLP and Deep Learning can give some sense, via rules, to language spoken by machines. NLP can be viewed as the bridge between machine language and the natural language of human speech, enabling machines to interpret and translate their language to human language by strictly following internal communication protocols.

Natural Language Processing, Natural Language Generation and How They Connect

What is NLG, and How Does it Relate to NLP and Other Forms of AI? explains how NLP and NLG use different technologies like Machine Learning, decision trees, support vector machines, Neural Networks, and Deep Learning to apply learning to available data. The DATAVERSITY® article Identify Data Patterns with Natural Language Processing and Machine Learning describes how NLP helps to uncover data patterns hidden in multi-structured and multi-source data, which is primarily textual data. All these treasures would have been left untapped without this powerful technology.

 

Photo Credit: a-image/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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