The Principles of Natural Language Processing
AI is considered superior to humans when it comes to data-driven decision-making activities but inferior to creative and cognitive tasks. However, in the past few years, language-based artificial intelligence has moved to a whole different level, changing the mind of many about what the technology is all about. The most recent and visible breakthrough of AI in the language is NLP (Natural Language Processing).
So, what is Natural Language Processing? NLP involves several steps that help computers process language similarly to humans. Learn more about Natural Language Processing, what it entails and its benefits in general.
Natural Language Processing: What is it?
NLP is basically a branch of computer science, majoring in artificial intelligence dealing with computers and their abilities to understand spoken words and text in the same way as human beings. Natural Language Processing work very hard to give computers the ability to understand voice data and text and respond with their own speech or text (as human being do).
This technology combines several other technologies like deep learning, statistical, machine learning and computational linguistic models. Together, they make it easy for computers to process human inputs in terms of voice or text data.
Natural Language Processing plays an important role in computer programming in that it translates text and voice from one language to another, summarizes text rapidly and responds to voice commands in real-time. You may be wondering where NLP in real life is. If you have used digital assistants, customer chatbots, speech-to-text dictation tools and voice-operated GPS, then you have interacted with NLP. Also, you will find Natural Language Processing in enterprise solutions to help boost employee productivity, streamline business processes and simplify business operations.
Tasks Involved in Natural Language Processing
It is difficult for computers to understand human language as it is filled with ambiguities. Therefore, it is hard to develop software accurate enough to know and understand the exact meaning of voice or text data input. Suppose natural language-driven apps are to be useful. In that case, programmers should teach them all human language irregularities that took humans years to learn, like metaphors, variation in sentence structure, sarcasm, homonyms, and grammar and usage exceptions. Natural Language Processing uses some tasks that make it easy for computers to understand what is fed into the system.
Some NLP tasks include:
It is also known as part of speech tagging. This process involves determining a piece of text or part of speech in a certain word based on its context and use. Part of speech in NLP will show the different meanings of the same work based on its position in the sentence and use. For example, “he can make pancakes” and “what is the make of your car?”, “make” in the first sentence is a verb and a noun in the second sentence.
Also known as speech-to-text. Works by converting voice data input into text. Speech-to-text is essential in all apps that answer spoken questions or follow voice commands. However, speech recognition has a few challenges that revolve around how people talk (different intonation, talking quickly, different accents, and incorrect grammar and slurry words).
This task involves identifying when and if two different words talk about the same entity. A good example is when using a pronoun to refer to an object or person. For example, the co-reference resolution will determine if the pronoun used refers to an object or person (‘’he’’ = ‘’John’’, ‘’it’’ = ‘’car”). It can also identify idioms in sentences like the word “bear” can mean an animal or a large hairy person.
So, is Natural Language Important?
Natural Language Processing is essential in almost all computer system activities, especially when you want to hear a response or decision from a dialogue system or when you want an AI system to work as per your instructions. This makes NLP a very important part of any AI system and enterprise in general.
Structuring Unstructured Data Source
Human language is very diverse and astoundingly complex – humans express themselves in many different ways, both in writing and verbally. There are hundreds of dialects and languages, each with distinctive syntax rules, grammar, slang and terms. When talking, some people may stutter, mumble, borrow words from other dialects or have regional accents. Also, when writing, people often abbreviate some words, misspell them, or omit punctuation.
It is essential to have a syntactic and semantic understanding considering the fact that deep learning is widely used for human language modelling, hence Natural Language Processing. NLP plays an important role in resolving ambiguities in human language. Also, it adds relevant numeric structure to different downstream applications’ data like text analytics and speech recognition.
Help with textual data
NLP helps computers scale language-related tasks and simultaneously allows computers to communicate with humans using their language. For instance, with the help of Natural Language Processing, computers can hear speech, measure sentiments, read text, interpret it and determine the important parts of a sentence.
Thanks to NLP, machines can now consistently analyze more human-based language data without being biased and fatigued despite the production of large unstructured data daily.
In a Nutshell
The growth that Natural Language Processing undergoes makes it an important part of consumer and business applications. Therefore, the need to use NLP in all activities, from law to medicine, in order to understand human language. This technology makes business processes easy as it offers its tireless ability to make sense of human words. Don’t be the odd one out!