Understanding human language is a different thing but absorbing the real intent of the language is an altogether different scenario.
Although computers could process multiple queries at once were versatile, multitaskers and what not but they lacked something. And yes that something was “understanding of the human emotions”, it won’t be an exaggeration to say what appeared like an alien concept in the past has become a “reality of the present”.
Now computers can analyze the tone of the speaker, can detect sarcasm from the sentences and are even capable of making automated summaries from long pieces of text, and the technology that is pushing computers to do all that today is Natural Language Understanding.
Let's understand the concept of NLU via this blog.
Natural Language Understanding (NLU)
Natural Language Understanding is a part of the broad term Natural Language Processing. NLU derives the "actual meaning" from a given query, it further helps computers to develop an understanding of the human language.
Voicebots, message bots comprehend the human queries via Natural Language Understanding. NLU focuses on the “semantics” of the language, it can extract the real meaning from any given piece of text.
Although voicebots have become extremely advanced in the modern era even then it is difficult for them to process high level queries i.e. a voicebot will find it difficult to process the queries that are a little twisted and conveys a slightly different meaning than the “usual ones” and from there the idea of NLU gained recognition.
NLP could process simple queries like “I am hungry and I want pasta” but it's only via NLU the computer will develop the understanding to process queries like” I want something Italian for dinner today.”
(Related Blog:- 4 Differences between NLP and NLU)
As NLU enables the computers to understand complex level emotions, it is used in sentiment analysis (extracting the user’s opinion via analyzing their comments) and sarcasm detection (detecting whether a given text is sarcastic or simple), without NLU it would have been practically impossible to process these high level queries.
NLU enables a computer to understand human languages, even the sentences that hint towards sarcasm can be understood by Natural Language Understanding (NLU).
Learn more about Natural Language Understanding via this video.
As now you have understood the basics of NLU, lets learn about the steps followed in Natural Language Understanding.
Steps of Natural Language Understanding
According to the traditional system there are three steps in natural language understanding. Every step focuses on “semantics” of the language.
Giving commands to voicebots comes in the first step. Dialogue exchanges like “show me the best recipes”, “play party music”, comes in the first level of understanding.
Compositional semantics focus on a group of sentences. In this step NLU groups the sentences, and tries to understand their collective meaning. Based on the previous logic, NLU tries to decipher the meaning of combined sentences.
Understanding the collective meaning of dialogues like “show me the best recipes” is connected to food is the level of understanding computers develop in this step.
“Lexical Semantics” studies the meaning of individual words and phrases. For a given sentence “show me the best recipes”, the voicebot will divide it into five parts “show” “me” “the” “best” “recipes” and will individually focus on the meaning of every word.
It will derive meaning of every individual word and will later combine the meanings of these words. It will process the queries based on the combined meaning and show results based on the meaning of words.
Three steps of natural language understanding
4 Applications of Natural Language Understanding
(Please note that Kwantics.com is used as reference for this section)
Voicebot:- Natural Language Understanding (NLU) has paved the way for human and machine interaction. Chatbots and voicebots like Siri, Cortana, and Alexa understand the human language; they use a combination of NLU and NLP for showing the desired results. NLU enables computers to understand human language.
When a human types a particular query the voicebot first understands the real intent of the query, breaks it down into computer understandable language and later proceeds with the query, everything from understanding to breaking the query into smaller parts has been made possible via Natural Language Understanding (NLU).
Sentiment Analysis:- Humans have multiple ways for communication, they sometimes camouflage their real emotions and make a “sarcastic comment”, other times humans convey their emotions indirectly. It might be easy to process normal queries but it is extremely difficult to understand the queries that have a “hidden meaning”.
(Related Blog: 7 Natural Language Processing Techniques for Extracting Information)
And that’s where Natural Language Understanding can help. NLU can be used for analyzing the emotions of disgust, sadness, anger from any given piece of text. Sentiment Analysis helps in understanding the tone of the speaker.
By analyzing any given piece of text, NLU can depict the emotions of the speaker. Sentiment Analysis is these days used widely in multiple industries, it can help in understanding customer reviews about a product.
These days Sentiment Analysis is being employed in multiple industries, it is used in Sales and Marketing to understand customer reviews. Customer reviews are analyzed via Sentiment Analysis and post analysis the data is delivered to the sales and marketing team of respective companies.
(Related Blog:- Introduction of Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA))
Question Answering: The modern day devices can communicate with humans via the human language. So you might be Indian, Japanese or Chinese but irrespective of the nationalities your Google Assistant can be your saviour.
Voicebots use NLU for question answering, Google Assistant can interpret 44 languages and it can process both verbal and written queries. So whatever your question is, Google Assistant is your place to be. Based on NLU it will skim through its entire history and will bring forward the most appropriate answers to your questions.
Automatic Summarization:- Summarization is the core of understanding bigger and broader concepts in the shortest time span. NLU can help in automatic summarizations of long pieces of text.
NLU enables computers to skim through long pieces of text. Post skimming computers can prepare a summary of the important information. Automatic summarizations are extremely helpful for people who are looking for concise and lucid explanations.
NLU can also be used in sarcasm detection, high level machine translations , and automated reasoning.
Applications of Natural Language Understanding
Companies and NLU service models
NLU is no more an inflated concept, it is the present day technology that can redefine the entire future. It can modify the work cases in multiple industries, it can perform many operations in the shortest possible time span. Most of the tech giants are building their own NLU systems. Let's take a look at the companies that are exploring the advantages of Natural Language Understanding.
This service specializes in domain customization and text analytics. Watson can be trained for the tasks, post training Watson can deliver valuable customer insights. It will analyze the data and will further provide tools for pulling out metadata from the massive volumes of available data.
Watson Natural Language Processing tool can be deployed anywhere. It can analyze text to extract concepts, entities, keywords, categories, semantic roles and syntax.
(Related Blog:- What is Google’s Open Source Language Interpretability Tool?)
Using LUIS developers can build their customized language models. Developers with no machine learning experience can also build their models via this service. This service is jampacked with prebuilt, entities, features and applications that can simplify the model building process.
(Related Blog: What is the Knowledge Graph?)
Models built using LUIS are always in the active learning stages, so even after building the entire language model developers can still improvise them from time to time.
These interfaces focus on maintaining a number of language models, the AI ready system can be used for flexible and scalable deployments via cloud. People without coding knowledge can also test and build language models via Saga’s NLU framework.
The innovative models will help in cutting down the costs, its prepackaged models can assist developers in building models.
“Machines take me by surprise with great frequency”- Alan Turing
In a nutshell, Natural Language Understanding “a branch of artificial intelligence”, a “subset of natural language processing”, can be used for real understanding of human language. NLU can process complex level queries and it can be used for building therapy bots.
In the future NLU might help in building “one click based automated systems” the world can very soon expect a model that can send messages, make calls, process queries, and can even perform social media marketing.