Natural language processing (NLP) is a branch of technology that handles the interactions between natural human languages and machines.
Natural language processing uses a combination of computational linguistics, machine learning, and deep learning models to train machines to understand speech and respond in much the same way as human beings would. In other words, the system can process and fully understand the input language (whether it be in text or voice format) and respond in a meaningful way by comprehending the writer’s intent and sentiment.
Natural language is filled with obscurities that make it difficult for machines to easily comprehend the meaning of the text or voice information provided. English, as an example, has many homonyms, idioms, metaphors, grammatical exceptions, homophones, and so on within its syntax. These intricacies are the reason it takes people years to learn a language and are what programmers must teach machines in order to make their interactions with their users meaningful. A simple thing as sarcasm can derail a software’s ability to decipher a person’s true intentions.
There are a few components that encompass natural language processing systems:
At its inception, it was nearly impossible for natural language processing applications to tend to a large volume of text and voice data due to a multitude of applications necessary to perform various rule-based natural language processing tasks needing to be hand-coding
For this reason, programmers began implementing statistical natural language processing, combining algorithms with deep learning and machine learning paradigms to quickly and automatically scrape, classify and tag segments of text and voice data. Once this process is completed, a statistical likelihood of each meaning the text may have is assigned to the data. Combining these technologies allows systems to learn as they work, continuously extracting more and more meaning from more significant volumes of unlabelled text and voice datasets.
Language translation: The most used form of natural language processing is probably within translation systems such as Google Translate. A good translation doesn’t begin and end by simply taking a word and replacing it with one in another language. Effectively translating a text involves truly understanding the meaning of the input text (with all its possible nuances) and translating it to contain the same impact as the output language.