Natural Language Processing (NLP) is 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.
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. 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 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.
Mordor Intelligence predicts that by 2026, the worldwide NLP market is projected to touch USD 42.04 billion, with a CAGR of 21.5%. The top 2022 use cases for NLP will be customer service chat bots, fake news detection, social media monitoring, multilingual NLP, and the use of supervised, unsupervised, and reinforcement learning in training models.
The 2021 Trends in NLP include two distinct types of developments:
- Trends that impact training models such as collaboration of supervised and unsupervised learning, use of reinforcement learning, accurate classification with Deep Learning (DL), and use of transfer learning to further tune the models.
- New NLP capabilities for market intelligence monitoring, custom recommendations, sentiment analysis for social channels, enhanced chat bots and virtual assistants, and semantic search
It is interesting to note that increasingly, NLP and NLG are collaboratively transforming the investment management sector with human-like assistance. For example, in the pre-trade phase, NLP and NLg are used to collect, analyze, and summarize data from multiple sources. Moreover, In-built AI technology can rationalize investment decisions and save time for the busy investment analysts.
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. Complete NLP Landscape from 1960 to 2020 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 report 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.
NLP has made great inroads in the healthcare and life sciences sectors, though the market growth was somewhat affected during the pandemic. While this period presented new threats to the global market players, it also uncovered new opportunities and new market segments requiring pharma research, drug development and so on.
Is NLP 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.
In a recent Forrester Report, two forms of NLG have been mentioned. The short-form NLG for “analytics:refer” to short-scrpted, automated NLG, which cannot be customized. On the other hand, the long-form NLG , which is template and rule based, accommodates full customization and frequent updates. The long-form NLG is suitable for generating lengthy, complicated content. Salesforce has announced an agreement to acquire Narrative Science, following which Tableau will be reinforced with additional NLG features. This is welcome news for existing Tableau customers as they will have access to “both short-form and long-form NLG depending on their use case.” Additionally, long-form NLG will play a major role in enterprise BI platforms.
According to the 2022 Global NLG Software Market Research Report, Covid-19 impacted this market in a significant way since the onset of Covid in China in 2019. Between 2016 and 2021, this market grew in millions of USD and hopefully this market growth pattern will continue through 2026. with a compound annual growth rate (CAGR).
NLP, NLG, and How They Connect
What is Natural Language Generation (NLG)? 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 article The Evolution of Natural Language Processing: 2021-2022 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.
Natural 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. Some technology barriers 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.
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