Natural Language Processing



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Natural language processing is the branch of artificial intelligence that deals with generating, understanding and analyzing the languages that humans naturally use in order to communicate with computers in both spoken and written ways using natural human languages instead of computer languages. Artificial intelligence is the engineering and science of constructing and building intelligent computer programs and machines. It does not limit itself to techniques that are biologically observable and relates to the idea of using computers to understand human intelligence.

NLP Interface

Recent approaches to NLP are based on machine learning, where data is fed into the learning machine with every action and non-action feeds and then the task gets automated without constantly requiring human or manual interference. Machine learning has allowed computers to find hidden insights, using algorithms that repeatedly acquire from data that is provided to them, without being programmed explicitly where to look. Most of the research being done on natural language processing revolves around search, especially enterprise search. It is the ability of a computer program to understand human speech as it is spoken.

NLP often referred to as computational linguistics has some tasks involved like named entity extraction, deep analytics, sentence segmentation, co-reference resolution, part-of-speech tagging, and parsing. It is the field that focuses on the interactions between computers and human language. It sits at the intersection of artificial intelligence, computational linguistics, and computer science. For any search using natural language processing, the program will itself recognize any abbreviated term or acronyms. The challenge lies where computers have to understand the way humans use and learn the language. Human language is neither too simple nor is it precise. For computers to understand human language, they need to understand the concepts and their linkage with the words to create a meaningful sentence. For example, consider a statement "Baby swallows fly." Now, this could have various meanings making it tough for the program to understand the exact meaning behind it. Like the word fly or swallow is used as a verb, that makes the baby as an adjective or a noun. In the course of human communication, the meaning of the sentences depend on both the understanding of each person’s ambiguity in human languages and the context in which it was communicated. This is where the software has to be programmed to understand linguistic and context structures.


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.

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