Can NLP Help Machines to Understand Human Language?
Do you know a vast amount of your business data goes unanalyzed? More than 80% of business data are unused because these data are in text or similar form i.e. not recorded in table and column manner. These are unstructured data and we are the source of these types of data – the human language.
Natural Language Processing is the branch of AI which helps companies or any industries to analyze the unstructured data. The rate of unstructured data grows about 40-50% every year. How far can NLP process our language and read our patterns? Is this 100% achievable? Is NLP going to make machines grant the world of human emotions and let it gain an upper hand from us?
Let me guide you through this article and help you find the solutions for the above concerns. A new world of information awaits you in the following sections.
NLP and machine learning:
Machine learning can be called as a data analysis model that learns, identifies patterns, and makes decisions based on the collected data. This is an automated process that doesn’t require much extrinsic help from humans.
Why did I italic those two words while defining machine learning?
You guessed that right. There is a reason for every action, let me mention the words again if you haven’t yet struck with the idea – “Collected data”.
Compare these two words with the facts mentioned in the opening section of this article. The unstructured data goes unused and it cannot be pledged as “collected data” (cross fingers). In order to process complete data analysis, we need to feed all the data with machines. With NLP, machines will be able to find more useful patterns and that’s the channel to process human languages.
NLP helps machines or computers to understand human’s natural language, our language doesn’t make any sense for these machines, what they need is information with data labels.
The messages we send on WhatsApp, telegram, etc. are unstructured data that we sent as texts.
The machines are fed with these data by following different processes and let it recognize the meaning or context of the text. Text-to-speech engines are the best example of NLP.
Suppose a person is chatting with his friend and as the conversion progresses, the level of emotion begins to slide different sides. From the normal chit chat to happiness, then to sober, and finally anger.
“I CAN’T HELP YOU”
That was his final conversation when you look through the chat history, you may recognize the anger expression. From the normal lowercase to a sudden change of the above text in uppercase expresses his anger. Can the machine find his subtle changes of human emotions?
Machines recognizing our emotions are still far away from reality however, it’s very much achievable!
How Do Machines Learn the Human Pattern From the Textual Content?
An algorithm, yes, the machines recognize the patterns of human data by following an algorithm. To reach the target (analyzing unstructured data), we provide a set of instructions which are known as algorithms. With many such instructions, the machine becomes smart enough to make its own decision.
No human intervention is then needed to make further decisions. However, in some cases, the machine receives supervised learning to proceed from the initial stage. NLP consists of two components – NLU and NLG.
Natural Language Understanding (NLU) maps the input to analyze different aspects of the language. It is then converted into useful phrases and processes further with Natural Language Generation (NLG). Understanding is always a herculean task than generating or in human terms, retrieving.
Ever wonder what dogs try to convey with barking? It barks but we don’t understand their communication. However, if we’ve lived with a pet dog and taken care of it then we do understand and recognize certain patterns from our experiences. Like when an outsider comes in, they start to bark, they lick our faces or hands to express their love. We understand it either by self-learning and then recognizing or else from the instructions we received from others which are nothing but supervised learning.
I wonder when humans are going to find a machine that studies and translates the dog-language!
Two Main Techniques Used in NLP:
The two main techniques used to develop Natural Language Processing are syntactic analysis and semantic analysis. These two techniques help to understand the natural language where the syntax is meant for grammatical structure and semantics checks the conveyed meaning.
The rules of grammar are analyzed from the natural language and help to recreate them for the machine’s interaction. The interpretation of words and their conveyed meaning is analyzed semantically for a machine’s better understanding. It’s a tough process and has a complicated level of understanding.
Will NLP Make the Machine Upper Hand Over Human Beings?
Machine learning became a reality when the machine started to get enough data. Machines then started to perform useful insights like demand forecasting, predictive analytics, KPI analyzers, business gap analysis, etc.
If this can be performed with proper instructions then machines that recognize human languages with proper emotional patterns are no longer distant in the future. Image samples on various expressions and reactions based on each emotion can be fed to the machines. The machine then starts image recognition by scanning our facial expressions. If such cameras are installed around us then won’t that be possible?
If so, what will be the status of our privacy? If it harms us in an adverse manner then isn’t that harassment or harmful access to our personal life? Won’t the criminals and burglars take advantage of such situations?
Let’s wait for the future to witness what’s going to happen in the next few years.