NLP, which stands for Natural Language Processing, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language to process and understand human language in any given field so that they can Automatically perform duplicate tasks such as machine translation, summarizing ticketing, and more.
Take emotion analysis, for example, which uses natural language processing to recognize emotions in a text. This classification task is one of the most fundamental challenges of NLP and is often used by businesses to automatically identify brand emotions on social media.
Human language is complex, ambiguous, chaotic, and varied. There are more than 6,500 languages in the world, all of which have their own syntactic and semantic rules. Therefore, in order for machines to understand natural language, it first needs to be converted into data that they can interpret, which is called data preprocessing.
What does NLP natural language processing refer to and how does it work?
Natural language processing helps computers communicate with humans through their language and scales other language-related tasks. For example, NLP allows computers to read text, listen to a speech, interpret it, measure emotions, and determine the importance of certain parts.
Today’s devices can analyze language-based data more than humans without fatigue and in a consistent and unbiased manner. Given the staggering amount of unstructured data that is generated every day from medical records to social media, it is crucial to create an automation system for complete text and speech data analysis.
Natural language processing includes many different techniques for interpreting human language, from statistical methods and machine learning to rule-based and algorithmic approaches. We need a wide array of approaches because text-based data and audio, as well as practical applications, are very different.
The basic tasks of NLP include marking and parsing, rooting, tagging part of speech, language recognition, and identifying semantic relationships. If you used to describe sentences in diagrams in high school, you would probably do them by hand. In general, the task of the NLP is to break down the language into shorter, more basic sections, to try to understand the relationships between them, and to examine how the sections work to create meaning.
principles of natural language processing
Natural language processing enables you to do everything from text classification and data extraction to translating text from one language to another and summarizing long content. In NLP data preprocessing, syntax and semantic analysis are key to understanding the grammatical structure of a text and identifying how words relate to each other in a particular context. However, turning text into something that machines can process is complicated.
There are several methods that can be used to “clean up” a data set and further organize it, including:
Encryption: Divides text into smaller semantic units or unit clauses.
Labeling a part of speech: Marking words as nouns, verbs, adjectives, adverbs, pronouns, etc.
Find the root of words: Standardize words by reducing them to their root forms
Remove prepositions: Filter common words that do not add unique or useful information, for example, prepositions in an article such as in, to, one, comma, or other items.
Various applications of NLP
We do not constantly think about the complexities of our language. It is an intuitive behavior used to convey information and meaning through semantic cues such as words, cues, or images. It has been said that learning a language is more naturally easier in adolescence because it is a repetitive and trained behavior, just like walking.
However, it is very difficult for computers to understand what happens naturally to humans with unstructured data volumes, lack of formal rules, and lack of real-world context or intent. For this reason, machine learning and artificial intelligence (AI) have become more and more important as humans become more dependent on computing systems to communicate and perform tasks.
As AI becomes more sophisticated, so will natural language processing (NLP). While the terms AI and NLP may evoke images of futuristic robots, there are now basic examples of NLP in our daily lives, such as email filters, smart assistants, chatbots, targeted search results, forecasting. There is text, language translation, digital telephone calls, data analysis, and text analysis. To see different examples, you can refer to source number 3 of this article, or to learn and increase your practical and coding skills, refer to free training courses, as well as competitions related to this topic, the famous Kaggle data science company.
Natural language processing is a way for a computer to understand and process human language. In today’s world, there is a need to use intelligent methods to increase quality and speed in doing things without physical interference, and only the use of human thinking and intelligence can lead to the invention of new and efficient methods such as NLP to prevent duplication of work. Be.