By employing NLP, developers can structure and organize knowledge to perform tasks such as translation, relationship extraction, automatic summarization, sentiment analysis, topic segmentation, named entity recognition, and speech recognition. NLP algorithms are mainly derived from machine learning algorithms and NLP can rely on machine learning instead of hand-coding large sets of rules, to automatically learn these rules by examining a set of data like a book, a collection of sentences from a large corpus of data making a statistical inference. So basically the more accurate a model will be if it analyzes maximum data. NLP is used to study text letting machines to comprehend how humans interact. This computer-human interaction enables real-world applications like sentiment analysis, part-of-speech tagging, automatic text summarization, relationship extraction, named entity recognition, topic extraction, stemming, and more. NLP has its uses mainly in machine translation, text mining, and automated question answering.
Other than these some common applications of NLP are : spelling and grammar checking, optical character recognition (OCR), lexicographers’ tools, screen readers for blind and partially sighted users, document clustering, information retrieval, question answering, exam marking, machine translation, document classification (filtering, routing), information extraction, text segmentation, report generation (possibly multilingual), email understanding, augmentative and alternative communication, dialogue systems, machine-aided translation and dialogue systems. NLP is used to examine parts of a sentence to fully understand the grammatical structure of a sentence. It involves the implementation of advanced data processing techniques to data sets to extract specific information from them. Deep analytics is often used in the pharmaceutical sector, the scientific community, financial sector and biomedical industries. NLP is used alot for programs for machine translation in which a human language is translated automatically into another human language. In data mining, a named identity, that describes one item from other sets of items is extracted that have similar attributes like age, company names, phone numbers, first and last names, addresses, email addresses, company names, etc. There are innumerable benefits of natural language processing like it can be leveraged by companies to improve the accuracy of documentation, the efficiency of documentation processes and recognize the most relevant information from large databases.