Natural Language Processing
for interactions between humans and computers
What is Natural Language Processing?
The branch of Artificial Intelligence that helps computers read, understand and interpret human language is called Natural Language Processing. Abbreviated as NLP, this technology uses language interpretation to facilitate interactions between humans and computers.
The origins of Natural Language Processing can be traced back to the early 1950s, when punch cards were used to communicate with the first proto-computer machines. This was one of the earliest interactions between machines and humans. With the advent of modern computers and smart technologies, helping machines understand human language has now become much easier.
NLP makes the human language more meaningful for machines.
Did you know?
One of the earliest and most successful instances of a machine understanding human communication occurred in the early 60s through ELIZA (an old NLP program).
ELIZA was capable of human-like responses that were quite astonishing for scientists of that time when such breakthroughs were just beginning to occur in this field.
Applications of Natural Language Processing
The use of the Natural Langauge Process is common in applications where text is involved. NLP acts as a seamless interface that bridges communications between humans and machines with the help of linguistic intelligence and cognizance.
Key tasks handled by NLP include Text-To-Speech, Speech Recognition, Optical Character Recognition (OCR), Word Segmentation and Speech Segmentation.
Document reading and processing
Answering Commonly Asked Questions
Natural language generation
Natural language understanding
Grammatical error corrections
Key challenges while using NLP technology
Humans communicate with emotions that often determine the strength attached to the meaning of a language being used. It is difficult to convey the same emotions to a machine to make it understand conversations and get the desired response.
Some rules that control information transfer might be abstract for a computer – for example, sarcasm and metaphors.
Signifying the plurality of objects and theoretical concepts are major hindrances in Natural Language Processing used in AI.
Natural Language Processing also brings forth a certain level of ambiguity while information exchange occurs, especially when associated with inherent concepts.
Key Natural Language Processing techniques
Syntactical analysis
Syntactic analysis is the process of managing the arrangement of words in sentences to make grammatical sense.
In NLP, this analysis is used to determine how a language spoken by humans can make grammatical sense for a machine to understand.
Specific computer algorithms are designed to apply grammar rules to words being fed into a computer, for getting meaningful output from them.
Semantical analysis
Being of one of the most difficult factors associated with NLP techniques, semantics deals with understanding the meaning derived from a text or a speech.
Just like syntactic analysis, this method makes use of specially configured computer algorithms to derive meaning from words that carry grammatical sense.
Semantics is an area that still needs a lot of work in most technologies that use Natural Language Processing for information processing.
Morphological analysis
This technique mainly involves identifying the roots of words comprising information required by a computer to understand.
Speech tagging is used to tag words or parts of speeches, which are further stripped down to their base forms.
For example, while analysing a piece of text that contains words such as “running” and “ran”, morphological analysis algorithms will identify the root of these words as “run”, thus making machines understand the context better.
How does NLP work in Simplifai’s AI solutions?
As a creator of AI solutions in the form of Digital Employees, we use AI technologies such as NLP and Intelligent Process Automation to make our product modules understand human language.
We use NLP techniques to train our Emailbot, Documentbot, and Chatbot to read, interpret, analyze, and understand human language, to respond and undertake actions accurately.
In our solutions, a rules engine linked with different languages helps the bot programs dissect text right up to its bare form and tag the right kind of entities and intents.
With these actions, the bot can automate further tasks such as correspondence, document archival, system updates, replies, and much more. The desired results occur within no time at all, ranging from a few seconds to a maximum of few minutes.
Our robust language processing techniques can achieve automation as high as 90%.
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