A Beginner’s Guide To Natural Language Understanding

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”

– Alan Turing
The entire gamut of artificial intelligence is based on machines being able to ‘understand’ and ‘respond’ to human beings. Which is impossible without the capability of machines to interact with humans in their natural language, like other human beings. Moreover, understanding does not involve the mere exchange of information and data but an exchange of emotions, feelings, ideas and intent. Can machines ever do that? Well, the answer is affirmative and it is not even that surprising anymore. What is this miraculous technology that smoothly facilitates the interaction between humans and machines? It is Natural Language Understanding.

What is Natural Language Understanding?

Natural Language Understanding is a part of Natural Language Processing. It undertakes the analysis of content, text-based metadata and generates summarized content in natural, human language. It is opposite to the process of Natural Language Generation. NLG deals with input in the form of data and generates output in the form of plain text while Natural Language Understanding tools process text or voice that is in natural language and generates appropriate responses by summarizing, editing or creating vocal responses.
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Natural Language Understanding Vs Natural Language Processing

Natural Language Processing is a wide term which includes both Natural Language Understanding and Natural Language Generations along with many other techniques revolving around translating and analysing natural language by machines to perform certain commands.   

A Beginner's Guide To Natural Language Understanding UpGrad Blog

Examples of Natural Language Processing

Natural Language Processing is everywhere and we use it in our daily lives without even realising it. Do you know how spam messages are separated from your emails? Or autocorrect and predictive typing that saves so much of our time, how does that happen? Well, it is all part of Natural Language Processing. Here are some examples of Natural Language Processing technologies used widely:

  • Intelligent personal assistants – We are all familiar with Siri and Cortana. These mobile software products that perform tasks, offer services, with a combination of user input, location awareness, and the ability to access information from a variety of online sources are undoubtedly one of the biggest achievements of natural language processing.
  • Machine translation – To read a description of a beautiful picture on Instagram or to read updates on Facebook, we all have used that ‘see translation’ command at least once. And google translation services helps in urgent situations or sometimes just to learn few new words. These are all examples of machine translations, where machines provide us with translations from one natural language to another.
  • Speech recognition – Converting spoken words into data is an example of natural language processing. It is used for multiple purposes like dictating to Microsoft Word, voice biometrics, voice user interface, etc.
  • Affective computing – It is nothing but emotional intelligence training for machines. They learn to understand your emotions, feelings, ideas to interact with you in more humane ways.
  • Natural language generation – Natural language generation tools scan structured data, undertake analysis and generate information in text format produced in natural language.
  • Natural language understanding – As explained above, it scans content written in natural languages and generates small, comprehensible summaries of text.
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Best tools for Natural Language Understanding available today

Natural Language Processing deals with human language in its most natural form and on a real-time basis, as it appears in social media content, emails, web pages, tweets, product descriptions, newspaper articles, and scientific research papers, etc, in a variety of languages. Businesses need to keep a tab on all this content, constantly. Here are a few popular natural language understanding software products which effectively aid them in this daunting task.

  • Wolfram – Wolfram Alpha is an answer engine developed by Wolfram Alpha LLC (a subsidiary of Wolfram Research). It is an online service that provides answers to factual questions by computing the answer from externally sourced, “curated data”.
  • Natural language toolkit – The Natural Language Toolkit, also known as NLTK, is a suite of programs used for symbolic and statistical natural language processing (NLP) for the English language. It is written in the Python programming language and was developed by Steven Bird and Edward Loper at the University of Pennsylvania.
  • Stanford coreNLP – Stanford CoreNLP is an annotation-based NLP pipeline that offers core natural language analysis. The basic distribution provides model files for the analysis of English, but the engine is compatible with models for other languages.
  • GATE (General Architecture for Text Engineering) – It offers a wide range of natural language processing tasks. It is a mature software used across industries for more than 15 years.
  • Apache openNLP – The Apache OpenNLP is a toolkit based on machine learning to process natural language text. It is written in Java and is produced by Apache software foundation. It offers services like tokenizers, chucking, parsing, part of speech tagging, sentence segmentation, etc.
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Applications of Natural Language Understanding

As we have already seen, natural language understanding is basically nothing but a smart machine reading comprehension. Now let’s have a close look at how it is used to promote the efficiency and accuracy, while saving time and efforts, of human resources, which can then be put to better use.

  • Collecting data and data analysis – To be able to serve well, a business must know what is expected out of them. Data on customer feedback is not numeric data like sales or financial statements. It is open-ended and text heavy. For companies to identify patterns and trends throughout, this data and taking action as per identified gaps or insights, is crucial for survival and growth. More and more companies are realizing that implementing a natural language understanding solution provides strong benefits to analysing metadata like customer feedback and product reviews. Natural language understanding in such cases proves to be more effective and accurate than traditional methods like hand-coding. It helps the customer’s voice to reach you clearer and faster, which leads to effective strategizing and productive implementation.
  • Reputation monitoring –  Customer feedback is just a tip of the iceberg as compared to the real feelings of customers about the brand. As customers, we hardly participate in customer survey feedbacks. Most of the real customer sentiments hence are trapped in unstructured data. News, blog posts, chats, and social media updates contain huge amounts of such data which is more natural and can be used to know the ‘real’ feelings of customers about the product or service. Natural language understanding software products help businesses to scan through such scattered data and draw practical inferences.
  • Customer service – Natural Language Understanding is able to communicate with untrained individuals and can understand their intent. NLU is capable of understanding the meaning in spite of some human errors like mispronunciations or transposed letters or words. It also uses algorithms that break down human speech to structured ontology and fishes out the meaning, intent, sentiment, and the crux of human speech. One of the most important goals of NLU is to create chatbots or human interacting bots that can effectively communicate with humans without any human supervision. There are various software products like Nuance which are already involved in customer interaction.
  • Automated trading – Capital market trading automation is not a new phenomenon anymore. Multiple software products and platforms are now available that analyse market movements, the profile of industries and financial strength of a company and based on technical analysis design the trading patterns. Advanced Natural Language Understanding tools which scan through various sources like financial statements, reports, market news are the basis of automated trading systems.
  • Market Intelligence – “What are competitors doing?” is one of the most critical information businesses need on a real-time basis. Information influences markets. Information exchange between various stakeholders designs and redesigns market dynamics all the time. Keeping a close watch on the status of an industry is essential to developing a powerful strategy, but the channels of content distribution today (RSS feeds, social media, emails) generate so much information that it’s been increasingly difficult to keep a tab on such unstructured, multi-sourced content. Financial markets have started using natural language understanding tools rigorously to keep track of information exchange in the market and help them reach it immediately.

A Beginner's Guide To Natural Language Understanding UpGrad Blog
Due to such varied functions carried out by natural language understanding programs, its importance in trade, business, commerce and the industry is ever increasing. It is a smart move to learn natural language understanding programs to ensure yourself a successful career.
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What is the best way to learn Natural Language Understanding?

The best way to prepare yourself for a brighter future in technological endeavors is to understand the algorithms of Artificial intelligence. The Post Graduate Diploma in Machine Learning and AI by UpGrad offers a chance to master concepts like Neural Networks, Natural Language Processing, Graphical Models and Reinforcement Learning. The most unique aspect of this course is the career support. And, the industry mentorship, which will help you prepare yourself for intense competition in the industry, within your actual job. So, let’s learn to use software products widely used in industry mentioned earlier like NLKT. This program aims at producing well-rounded data scientists and AI professionals with thorough knowledge of mathematics, expertise in relevant tools/languages and understanding of cutting-edge algorithms and applications.
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