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Over the years, technology innovations are being developed and the possibility of an AI machine to successfully interact like a human is nearly achievable. To do such, Artificial Intelligence is very keen in gathering the accurate learning  data that the machine needs.


Considering that machines are programmed using definite and precise algorithms, it is still a big question on how machines can understand human language wherein definite data are being fed to it.


On our previous articles, we have discussed that artificial intelligence being envisioned to create a machine that thinks, acts and reacts like a human-being, and as time passes, there are significant breakthroughs that encompasses all of the needs to achieve the total learning that our early scientists envisioned.


Traditionally, these data are very precise wherein most of the AI machines are being used in an exact and accurate command in order to achieve the desired action. However, the thought composition or deep learning of the machine has been given a value in programming in recent time. This kind of learning allows the machine to create a memory that would satisfy all of the human activity and commands it encounters.

One of the main components of making an AI machine successfully interact with human-being is Natural Language Processing or NLP.


Natural Language Processing is the key technique on teaching the machines on how to communicate, understand and properly respond to human’s natural language. While humans can easily master a language, the ambiguity and imprecise characteristics of the natural languages are what make NLP difficult for machines to implement. This kind of technique allows the machine to comprehensively understand the human language and requires data in understanding various words, approaches and concepts on how to deliver and connect intended messages.


Let us take Eliza for example, Eliza was an early natural language processing program created to demonstrate communication between humans and machines in the early 60’s. It simulated conversation by using a 'pattern matching' and substitution methodology that gave users an illusion of understanding on the part of the machine program, apparently it doesn’t have built in framework for scrutinize situations.  Interaction directives were provided by ‘scripts’ which allowed Eliza to process user inputs and engage in communication and direction on the script.


Traditionally, computers require humans to speak to them in a programming language that is very precise, highly structured and clearly enunciated voice commands. However, human speech  is not always as definite because of the linguistic structure that depends on many complex variables including language accents, slang, dialect and social context.


As time changes, NLP started using deep learning, wherein the AI examines and uses patterns in data to improve the machine’s understanding. This learning requires massive amount of labeled data to train, identify and assembling big data set.


There are two main techniques used in natural language processing. First is the syntax analysis which is the arrangement of words in a sentence to make a grammatical sense. It is used to assess the meaning from a language based on grammatical rules. This includes the techniques of grammatical analysis, word segmentation, sentence breaking, morphological segmentations and stemming. Second is the semantics which involves the use and meaning behind the words. Natural Language Processing applies algorithms to understand the meaning and structure of sentences. Using techniques word sense to prevent confusion on the contexts, recognition of name entity and natural language generation.

This makes NLP play a critical role in supporting machine-human interactions. We expect to see more breakthroughs that will make machines smarter at recognizing and understanding the human language.