Natural Language Processing (NLP)

• The ability of computer software to comprehend spoken and written human language is referred to as natural language processing (NLP). It is a component of artificial intelligence.
• Considering its origins in the study of languages, NLP has been around for more than 50 years. It has several practical uses in a wide range of industries, such as business intelligence, search engines, and medical research.
• NLP analyses the structure and meaning of text using either rule-based or machine learning techniques. It is a component of enterprise software, chatbots, voice assistants, text-based scanning programs, translation tools, and enterprise software that facilitates corporate operations, boosts productivity, and streamlines various procedures.

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How does natural language processing work?

NLP uses many different techniques to enable computers to understand natural language as humans do. Whether the language is spoken or written, natural language processing can use AI to take real-world input, process it and make sense of it in a way a computer can understand. Computers have microphones for collecting audio and programs to read, just as human beings possess various senses such ears and vision.? And just as humans have a brain to process that input, computers have a program to process their respective inputs. At some point in processing, the input is converted to code that the computer can understand.

There are two main phases to natural language processing: Data Preprocessing and Algorithm Development

Data Preprocessing

Data preprocessing involves preparing and cleaning text data so that machines can analyze it. Preprocessing puts data in a workable form and highlights features in the text that an algorithm can work with. There are several ways this can be done, including the following:

·?????? Tokenization : Tokenization substitutes sensitive information with nonsensitive information, or a token. Tokenization is often used in payment transactions to protect credit card data.

·?????? Stop word removal : Common words are removed from the text, so unique words that offer the most information about the text remain.

·?????? Lemmatization and stemming : ?Lemmatization groups together different inflected versions of the same word. For example, the word "walking" would be reduced to its root form, or stem, "walk" to process.

·?????? Part-of-speech tagging : ?Words are tagged based on which part of speech they correspond to such as nouns, verbs or adjectives.

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Algorithm Development

After preprocessing the data, an algorithm is developed to process it. Natural language processing algorithms come in many different forms, however these two primary varieties are frequently employed.

·?????? Rule-based system : The language rules in this system have been carefully designed. This method is still in use nowadays, having been employed early in the development of natural language processing.

·?????? Machine learning-based system :? Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they're fed and adjust their methods as more data is processed. Using a combination of machine learning, deep learning and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning.

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Natural Language Processing Used

The following are some of the main NLP activities and roles that natural language processing algorithms perform out there:

·?????? Text classification: Utilizing tags, this function classifies texts into distinct categories. Sentiment analysis, which aids the natural language processing algorithm in identifying the sentiment or emotion underlying a text, may find application for this. For instance, the system can identify how many favorable and bad references there were of brand A when it appears in X texts. It can also be useful for intent detection, which helps predict what the speaker or writer might do based on the text they're producing.

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·?????? Text extraction: The function that automatically identifies key information in text and summarizes it. Keyword extraction is one technique that does this; it extracts the most significant terms from the text and can be helpful for search engine optimization. Natural language processing doesn't do this entirely automatically; some programming is needed. However, there are plenty of simple keyword extraction tools that automate most of the process? the user just sets parameters within the program For instance, a tool may highlight the words that are used in the text the most. Another example is entity recognition, which extracts the names of people, places and other entities from text.

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·?????? Machine translation: In this process, a computer translates text from one language, such as English, to another language, such as French, without human intervention.

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·?????? Natural language generation: This process uses natural language processing algorithms to analyze unstructured data and automatically produce content based on that data. One example of this is in language models like the third-generation Generative Pre-trained Transformer (GPT-3), which can analyze unstructured text and then generate believable articles based on that text.

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Numerous real-world applications use the functions mentioned above, such as the following:

·?????? Customer feedback analysis: Tools using AI can analyze social media reviews and filter out comments and queries for a company.

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·?????? Customer service automation: Speech recognition technology allows voice assistants on customer service phone lines to comprehend what customers are saying and appropriately route their calls.

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·?????? Automatic translation: Text, audio, and documents can be translated into different languages using programs such Translate Me, Google Translate, and Bing Translator.

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·?????? Academic research and analysis: AI-powered tools are able to examine vast volumes of academic literature and research papers by analyzing both the text's metadata and content itself.

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·?????? Healthcare record analysis and classification: AI-based technologies can leverage insights to forecast and, ideally, prevent disease.

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Benefits of natural language processing

Enhancing human-computer communication is an important advantage of natural language processing (NLP). Code, called the computer's language, is the most direct means of controlling a computer. Human-computer interaction becomes considerably more natural when computers are able to comprehend human language.

Other benefits consist of the following:

·?????? Provides enhanced documentation efficiency and accuracy.

·?????? Enables an organization to use chatbots for customer support.

·?????? Provides an organization with the ability to automatically make a readable summary of a larger, more complex original text.

·?????? Enables both organized and unstructured data analysis for enterprises.

·?????? Enables personal assistants such as Alexa to understand the spoken word.

·?????? Makes sentiment analysis more manageable for businesses.

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Challenges of natural language processing

Natural language processing has many difficulties, the most of which stem from the fact that natural language is ambiguous and constantly changing. Among them are the following:

·?????? Precision: Traditionally, people have had to communicate with computers using a restricted set of precisely spoken voice instructions or a precise, unambiguous, and highly organized programming language. Human speech, on the other hand, is frequently ambiguous and its linguistic structure can be influenced by a wide range of intricate factors, such as social context, regional dialects, and slang.

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·?????? Language usage evolving: Language and human language usage are dynamic, which presents a challenge to natural language processing. While there are norms in language, they are not set in stone and can change throughout time. With the changing nature of real-world language, hard computational principles that are currently effective may become outdated in the future.

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·?????? Bias: NLP systems can be biased when their processes reflect the biases that appear in their training data. This is an issue in medical fields and hiring positions, where a person might be discriminated against.

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Real-world use cases of Natural language processing from Asia

Customer Support Chatbots:

In many Asian countries, large enterprises and e-commerce platforms utilize NLP-powered chatbots to enhance customer support services. These chatbots are trained to understand and respond to customer queries in various languages spoken across the region, such as Chinese, Japanese, Korean, and Hindi. By leveraging NLP techniques like sentiment analysis, intent recognition, and entity extraction, these chatbots can efficiently handle a wide range of customer inquiries, including product information, order tracking, and troubleshooting. For example, companies like Alibaba in China and Rakuten in Japan have deployed advanced NLP-based chatbots to provide round-the-clock assistance to their customers, improving user satisfaction and reducing the workload on human support agents.

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Real-world use cases of Natural language processing from USA

?Healthcare Information Extraction:

In the United States, healthcare organizations and research institutions leverage NLP for information extraction from clinical documents, medical records, and scientific literature. NLP algorithms are trained to parse unstructured text data and extract valuable insights, such as patient demographics, medical conditions, treatment plans, and research findings. By automatically extracting structured information from unstructured text sources, NLP facilitates tasks like electronic health record (EHR) coding, clinical trial recruitment, and biomedical literature mining. For instance, companies like IBM Watson Health and Google Health utilize NLP-powered solutions to analyze vast amounts of medical text data, enabling healthcare providers and researchers to make data-driven decisions, improve patient outcomes, and accelerate medical research efforts.

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Conclusion:

Natural Language Processing is a dynamic and rapidly evolving field with vast potential to revolutionize how we interact with computers and process vast amounts of textual data. By understanding the core concepts and techniques of NLP, researchers, developers, and practitioners can harness the power of language to build innovative applications and address real-world challenges. As NLP continues to advance, it promises to unlock new opportunities for automation, personalization, and intelligence in various domains, shaping the future of human-computer interaction and AI-driven innovation.

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