Natural Language Processing (NLP) has been done by the human brain for ages and is now being done by computers since the 1950s. If you think about it, the whole process of processing the language is quite complicated. Scientists and developers have been trying to make computers replicate what the human brain can do in minutes if not seconds. Natural Language Processing (NLP) kickstarted in 1954 when over 60 Russian sentences were auto-translated into English. Since then NLP is being progressively integrated into computer science and artificial intelligence to develop systems and software, capable of processing human languages. NLP is no longer limited to linguistics. Various other sectors today integrate NLP in their systems to enhance their processes. We are going to list down five industries and sectors that utilize NLP.
Understanding Natural Language Processing
NLP is the ability of computer software and programs to process natural human language both in text and in speech. It is a sub-field of Artificial Intelligence and linguistics. NLP’s progress can be traced from the use of symbolic NLP in the 1950s to the 1990s, to statistical NLP in the 1990s to the 2010s, to neural NLP at present. Neural NLP involves representation learning and deep neural network machine learning methods. Many experts predict that the use of such methods can improve language parsing and modeling. These can be used to improve the healthcare and education sector.
You might have been, unknowingly, using an application that uses NLP. Some common applications and tools that use NLP methods are:
- Grammar correction tools like Grammarly
- Plagiarism checking tools like SmallSEO
- SEO tools like Keyword Planner or Google analytics
- Google Translate
Significance of NLP
The significance of natural language processing lies in its applications in various sectors. Let us take a look at some of the sectors that get benefit from NLP.
1. Health Care
NLP can accurately process a large amount of data in a short period. Patient datasets contain information of all degrees. This data can be used to form assumptions, conclusions, as well as to gain insights about new variables. To optimize the medical dataset medical algorithms and machine learning can be applied to draw conclusions. This can result in discoveries about symptom patterns, patient behavior, diseases, and cures. Sentiment analysis, speech recognition, word sense disambiguation can be used for research in the mental health of humans. These can be used to draw conclusions that can help understand the emotions, thoughts, and feelings of a person. Pharmaceutical firms can also use NLP along with medical datasets to derive valuable conclusions that can be used for clinical trials and the formulation of new medicines.
Finance is another sector that utilizes NLP. NLP is being used for risk assessments, credibility analysis, sentiment analysis, customer chatbox, portfolio selection, etc. Named Entity Recognition, which involves mapping individual names from a set of text and detecting types of entities, can be used to track compliance processes and their status. Entity linking together with relationship extraction can be used to build and maintain relationships with stakeholders. Sentiment Analysis and speech recognition can be used for risk assessment and to determine credibility. NLP is used to predict and closely analyze the stock markets for better conclusions. It is also being used to build result-oriented portfolios.
Students, as well as teachers and professors, can get benefit from the integration of NLP in education. NLP is being used to detect plagiarism and the grammar of the textual language. This helps teachers to assess the thesis, dissertation, and assignment papers. It can also guide students into making the context more readable and relevant. NLP is also beneficial for learning languages. Several applications use NLP to offer writing and speaking functions in various languages. Along with that, the automatic translators offered by search engines like Google are being widely used to find definitions, synonyms, and antonyms of difficult words in different languages.
NLP can also be used to draw conclusions for surveys and important studies. It can also help researchers to identify areas for future research. Argument Mining can be used to extract and identify the premise, conclusion, and arguments from datasets. Automatic summarization can help academics with their research.
There are several applications of NLP that help businesses to increase the productivity of their processes and provide robust customer support services. NLP backed with big data can help businesses make more customer-oriented decisions. It can also be used to predict consumer behavior that can be used to design and market better products and services. Statistical NLP, NER, Optical Character Recognition, and other NLP techniques can also help businesses provide customizable products and services. Businesses have been using customer support chatbox, customer service automation, and customer feedback analysis. These use speech recognition, speech segmentation, and topic segmentation & recognition to make the process smoother.
5. IT and Data Science
Big Data is the talk of every town. To make the most out of big data, data scientists and analysts can use various NLP methods. NLP can be used to do market research and analysis to gain market insights, text summarizers and analyzers can also be effectively utilized. It can also assist in data mining. NLP can help developers with computer-assisted coding making code cleaner and more readable with minimal bugs.
Just like with any other AI technology, NLP can also sound overwhelming. Through the past five decades, NLP has made significant progress and is being used across industries. With the increasing digitalization and prominence of computers and artificial intelligence, NLP will see exponential growth. NLP is being used in a variety of industries. It has helped in providing value-based healthcare to people and helped students to learn without being restrained by language. Businesses can also make decisions prioritizing customer satisfaction. The volatile stock markets can be analyzed more conveniently with the help of NLP. NLP can also be deployed for risk assessment and insurance frauds, and so on…!!
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