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What is Natural Language Processing? NLP Application, Works & Future

Think of a machine that possesses the technology to comprehend the text language as well as needed expertise to translate the same into some other language. This implies that the machine is as capable as humans when it comes to understanding text. Such machine capability may seem difficult for you to believe. However, the domain of Natural Language Processing (NLP) has made this happen in today’s world. The NLP enables a machine to apprehend natural human language through integrating the power of artificial intelligence, computational linguistics, and computer system science.

The foremost NLP application was machine translation from Russian to English back in the 1950s. But the outcomes at that time were not successful and, then, for decades, limits of this field were illustrated while discrediting the NLP field for years. Nevertheless, a ton of progress has been made with the NLP since the 1950s. Consequently, Natural Language Processing (NLP) evolved as a prominent force in economic as well as social development during the times of the World Wide Web going from interconnected systems to a massive repository of exploitable data.

We have come up with this guide to give insights into the working of NLP, some everyday applications of NLP, and an overview of this fascinating field.

What is Natural Language Processing or NLP Meaning?

Before hopping to any other insights, let us first quickly define Natural language processing (NLP). Natural language processing (NLP) refers to the capability of a computer system program that enables it to comprehend language spoken and written by a human. It is basically a fragment of artificial intelligence (AI) that has been existing for more than fifty years now and holds its roots in the domain of linguistics. Moreover, NLP has been resolving and providing solutions to a large number of real-world applications operating in a diverse range of fields, incorporating architecture industry, search engines, business intelligence, medical research, etc.

How does Natural Language Processing work? 

With the natural language understanding, NLP algorithms perform the following significant functions –

Text classification

Text classification is basically allotting the tags to texts in order to arrange them into categories. The NLP algorithm helps in analyzing the sentiment or emotion that lies in a text. Further, it can even be employed for intent detection. This helps in predicting what the speaker or writer might accomplish according to the text they are constructing.

Text extraction

Text extraction is the process of automatically concluding text and discovering essential pieces of data. For example, keyword extraction enables us to draw noteworthy words from the text, which can be considered sound for search engine optimization. NLP involves some automated programming to do the same.

Machine translation

Machine translation is the procedure where the computer system becomes capable of translating text from one language, say, German, to another language, say Japanese, without demanding any human intervention. The natural processing language has made this possible for every language in every corner of the world. 

Natural language generation

Natural language generation includes the usage of natural language processing algorithms for analyzing undeveloped data while automatically creating content depending on that data. One of the prevalent examples is in language models, for instance,GPT3. This can analyze an unstructured text and then develop credible articles based on that text.

Natural Language Processing Applications / NLP Machine Learning Examples

All the functions of natural language processing discussed in the previous section are utilized in numerous and diverse real-world applications, incorporating –

  • Analysis and examination of customer feedback, such as AI examining social media reviews
  • Automation of customer service, such as voice assistants employing speech recognition  at the different end of a customer service phone line in order to comprehend what the customer is communicating for directing the call precisely.
  • Automatic translation, such as the utilization of tools like Google Translate, Translate Me, Bing Translator, etc. 
  • Academic research and analysis, such as AI analyzing immense amounts of theoretical material and research papers that are based on the text itself instead of the metadata of the text
  • Medical records categorization Examination, such as Artificial Intelligence utilizing the insights for forecasting and ideally preventing disease
  • Plagiarism and Proofreading in the word processors, such as tools like Grammarly, Microsoft Word, etc.
  • Stock predictions and discernment into financial trading, such as making use of AI for examining comprehensive overviews of the company’s financial performance;
  • Talent recruitment in the field of human resources
  • Routine litigation tasks Automation, such as an artificially intelligent attorney.

With the natural language processing examples, applications, and functions discussed above, you can infer that the NLP can interpret free,  unstructured data and turn it into analyzable text. Furthermore, you can find an enormous amount of information held in free text files, for example, medical records of patients. Until the deep learning-based NLP models were constructed, all such information was not obtainable to computer-assisted analysis as well as could not be interpreted in any systematic way. However, with NLP analysts, now we can filter through tremendous amounts of free text for uncovering relevant information. 

On the other hand, sentiment analysis as the primary use case for natural processing programming has proven to be highly beneficial. Data scientists can easily evaluate comments on social media to witness how their business’s brand is functioning with the help of NLP sentiment analysis. They can also examine notes from customer support service teams to recognize domains where people desire the business to perform better. 

Future of NLP/Natural Language Processing

Time has changed for the NLP. With the dawn of sounder search engines, blogs,  e-commerce platforms, as well as social networks, society has begun to understand and accept NLP technology that provides these days the treasured help for every user. Natural Language Processing renders a youthful field that is full of commitments. It comes with international community support that keeps developing new algorithms, techniques, and resources. Several complex NLP tasks are getting improved through Deep Learning employed by recent outcomes. 

The assortment of artificial intelligence advancement and the Internet emergence made NLP reach a level that was inconceivable just a few years ago. Natural Processing Language serves as a game-changing technology that has the power to shape the future of the Internet. 

Conclusion on Natural Language Processing

Natural language processing has the power to intensify the manner computer systems and human beings interact or communicate with each other. The best and most straightforward way to manage and operate a computer system is through code i.e., the language of the computer. When we make computers comprehend the human language, it becomes highly interactive and much more intuitive for us to operate with computers. With the evolution of NLP, the following benefits and privileges have come in today’s world for humans – 

  • The efficiency and accuracy of documentation have been enhanced.
  • The larger and more complex original text can be summarized automatically. 
  • Many personal assistants like Alexa have been invented to apprehend spoken word.
  • Various organizations, individuals, and enterprises have introduced chatbots for providing seamless customer support services that involve sentiment analysis. 
  • One can acquire cutting-edge insights from data analytics that could not get examined in previous times because of data volume.

Also Check: What is Managed Hosting

Server Wala Team

The blogs are written by a team of in-house technical content experts. They handle everything from research to writing and publishing.

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