CIOs say that enterprises have long been handling different technology for the intelligent assistants’ topic; Natural Language Processing has been a gateway into this possibility
Organizations have keenly invested in intelligent assistants as a technology that gives the end-user proper context. Repeated trials have shown results on the efforts that are being made to get the best results out of spoken language abilities.
Most of these systems are dependent on Natural Language Processing (NLP). They help machines, and people communicate in normal language. Natural Language Generation (NLG) and Common Language Understanding (NLU) are both sub-segments of NLP. These terms are confused by end-users are they are, in fact, different phases of a particular cycle for imitating the human interaction in computers.
Natural Language Processing
Natural language processing in artificial intelligence manipulates the spoken language to receive a much more defined data in return. This could be as basic as identifying nouns in a sentence or as complex as detecting a human’s emotions for a movie, analyzing movie reviews, etc. In simpler terms, a device uses the NLP models to read to understand the language used by a human (frequently known as the NLP machine learning).
It is a blend of artificial language, computational linguistics, and computer science. NLP intends to enable machines and humans to speak in a common language, similar to a human to human conversation. An effective NLP system can analyze the query and its priority, segment it, decide upon a relevant task, and convert it into a language understandable to the user.
Most AI tech developed need to clear the Turing test; the machine/ tech needs to converse with a human and convince them that they interact with a fellow human. Clearing this test has been the most sought after result for computer science, and NLP systems are trying to achieve this goal.
Common Language Understanding (NLU)
A minor part of natural language processing is a common language understanding. Once the language has been segmented, it’s an excellent opportunity for the tech to analyze, detect meaning, and even complete sentiment analysis. NLU divides language into absorbable, smaller segments that are much easier to comprehend. This is done by the semantic and syntactic analysis of the content.
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It intends to understand the actual value of written content. Post conversion of speech into text by speech recognition software, NLU tech can demystify its value. Understandably, similar content will have a varied impact, or different words will have the same meaning, or a word’s/phrase’s significance changes with a different context.
Understanding the content with ambiguity due to the language’s structure and standards is the biggest issue faced by NLU software. Prominent applications embed profanity filtering and emotion detection, among other features.
Natural Language Generation (NLG)
This refers to generating natural language as output for the input of structured data. Some of the common NLG frameworks are Numerical and textual data, diagrams, pictures, etc. In the current scenario, NLG is a separate conversation as compared to NLP and NLU; however, it still works in collaboration in some applications. A chatbot is a good example of a software that uses NLP, NLU (for understanding input text), and NLG (for providing output text). Such programs work beyond standard template-based systems, and would have been created by a human with domain experience and knowledge that provides accurate and well-researched results in minimal time.
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Understanding the basic difference
The basic difference between the three can be as understood as NLP being the language when devices read and convert input data/ text into structured data. NLU is the understanding of the statistical and textual data gathered by the machines. NLG is the conversion of the structured data into text and converting data into human language. AI bots like Google Assistant, Siri, and Alexa blend NLG and NLU to interact with end-users.
Another significant difference between NLP and NLG is that the latter can create information-rich data compared to only text evaluation for creating insights by NLP.
