Natural language processing is a branch of artificial intelligence that aims to help computers understand human language input in the form of text or voice. NLP combines multiple disciplines, including computational linguistics, Applications of machine learning, deep learning, and statistics.
These technologies work together to essentially give computer software the ability to process and understand human language in the same way that another human being might, including its meaning, intent, and sentiment.
In addition, companies are increasingly using Natural Language Processing Techniques to create business solutions that help companies simplify processes, increase productivity and streamline operations.
Why Should Companies Use Natural Language Processing?
Human interaction is the driving force of most companies. Whether it's a physical store with inventory or a large SaaS brand with hundreds of employees, customers and businesses need to communicate before, during, and after a sale.
That means there are countless opportunities for NLP to step in and improve the way a business operates. This is especially true for large companies that want to track, facilitate, and analyze thousands of customer interactions to improve their product or service.
It would be nearly impossible for employees to record and interpret all of that data on their own, but technologies integrated with NLP can help do it all and more.
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Natural Language Processing Techniques
Here are some additional details about how natural language processing works in relation to certain tasks:
Speech recognition. This is the process of converting spoken voice data to text data. Speech recognition software breaks speech down into individual sounds, using algorithms to reconstruct meaning.
Disambiguation of the meaning of the word. In many languages, the same words can have different meanings depending on the context. Word Sense Disambiguation (WSD) is the process of analyzing language to distinguish between meanings. For example, 'finished seconds' and 'completed in last second'.
Coreference resolution. This process helps determine if and when words in text or speech refer to the same entity. A good example is solving which pronouns (he, she, they) refer to which person or object.
Analysis of feelings. Language often has many subtleties. NLP can be used to extract emotions and attitudes, whether positive or negative, from language.
Natural Language Processing Real-Life Examples
Smart search and predictive text: Features like autocorrect, autocomplete, and predictive text are so embedded into social media platforms and apps that we often forget they exist. Autocompletion and predictive text predict what you might say based on what you've typed, finish your words, and even suggest more relevant ones, similar to search engine results.
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Autocorrect can even change words based on typos so the overall meaning of the sentence makes sense. These features have the ability to learn and change based on your behavior. For example, over time, predictive text will learn your personal lingo and personalize itself.
Language Translation: One of the most common examples of NLP is translation. In the 1950s, Georgetown and IBM introduced the first NLP-based translation machine, which had the ability to automatically translate 60 sentences from Russian to English.
Translation applications available today use NLP and Machine Learning to accurately translate text and speech formats for most global languages.
Semantic search: A customer service-oriented example of NLP would be using semantic search to improve the customer experience. Semantic search is a search method that understands the context of a search query and suggests appropriate answers.
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For example, if you're on an e-commerce website and you're looking for a specific product description, the semantic search engine will understand your intent and show you other products you might be looking for.
Spelling checker: Wondering what are the best NLP usage examples that apply to your life? Spell check is one of many, and it is so common today that it is often taken for granted. This feature essentially notifies the user of any misspellings they have made, for example when setting a delivery address for an online order.
Email filters: Email filters are common examples of NLP that you can find online on most servers. Spam filters are where it all started: they discovered patterns of words or phrases that were linked to spam messages. Since then, the filters have been continuously updated to cover more use cases.
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Future applications of natural language processing
Natural language processing is a technology that many of us use every day without thinking about it. However, as computing power increases and these systems become more advanced, the field will only progress.
Here are some of the ways NLP could be advanced in the future:
More advanced assistants. Smart assistants are likely to become smarter, able to perform more functions, and communicate more naturally than they are today. We can rely on them for more tasks in our daily lives.
Greater integration. Natural language processing may be more ingrained in our lives. Both inside and outside the home, we may be communicating a lot more with computers.
More automation. We may see NLP automate more tasks. Chatbots and digital assistants could be used in more areas of customer service, answering inquiries, sending emails, and offering support.
Smarter perspectives. Big data and sentiment analysis will give us a deeper understanding of how people interact with technology, what they want from it, and how it can be improved.
Instant translations. Natural language processing could even allow us to communicate instantly with others, regardless of whether we share a language with them or not.
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Conclusion
Natural language processing is a form of artificial intelligence that helps computers read and respond by simulating the human ability to understand everyday language. Many organizations use NLP techniques to optimize customer service, improve the efficiency of text analysis by easily finding the information they need, and improve social media monitoring. For example, banks can implement NLP algorithms to optimize customer service; a large consumer products brand could combine natural language processing and semantic analysis to improve its knowledge management strategies and social media monitoring.
USM, a major Artificial intelligence development services in Frisco, provides clever AI and NLP solutions that comprehend human languages and provide more accurate responses to customer questions.
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