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What exactly is NLP?
In artificial intelligence, Natural Language Processing (NLP) is a discipline that allows computers to comprehend natural language – the ideas and phrases that individuals communicate – and to utilize that understanding to generate value.
However, although computers are excellent at dealing with and interpreting organized data (such as spreadsheets and data structures), they are less adept at reading unstructured data, such as raw text written in any human language (English, Poland, Russian, or other).
Natural Language Processing is concerned with devising methods that will aid in bridging this gap between the two languages. Not only does it allow computers to recognize specific words, but it also allows them to recognize how human people use language when they talk or type. Ambiguity, turns of phrase, spelling errors, shortcuts, dialects, personal peculiarities and others are among other things. By teaching computers to read unstructured human language content, we can educate them to discern what people truly intend when they write or say a word, as well as extract meaningful and important information from it.
Many of the programs people use daily are powered by Natural Language Processing (NLP). For computers to utilize and interpret words in the same way that humans do, NLP techniques are used in practically every program or function that includes human language. Search engines, spell checkers, translation software, and intelligent machines are just a few examples.
How to put NLP to use in a real-world situation?
We've seen a short history of the models that are used to execute NLP tasks, but hang on a sec, what are the steps that we must take to complete any NLP task? In addition, how do we choose one of the NLP methods stated above to use for vector generation? We can't merely feed text data into the computers, after all. Even before converting to vector format, it has to be treated, and text pre-processing comes in helpful in this situation.
Text pre-processing refers to the procedures that must be taken to change data before it can be fed into a computer. It is significant in a variety of ways. This will be better understood if we look at an example of Sentiment Analysis performed on a customer review obtained from Yelp.
An NLP job that includes evaluating text and extracting sentiment is called sentiment analysis (also known as sentiment extraction) (i.e. whether the text is with positive, negative, or neutral sentiment). Common words and quantitative values are removed from text throughout several of the pre-processing phases since they do not add significantly to the mood of the content. The accuracy of the sentiment analysis is frequently improved as a result of these pre-processing stages.
Benefits of Natural Language Processing (NLP)
1. Customer satisfaction
Artificial intelligence used throughout an organization may generate useful information that can be used to enhance consumer relations. For example, the hotel industry relies on surveys and reviews to better understand the behavior of its customers. Not only understands how consumers feel about the experience important but so is learning how they rate the experience. To identify sentiment in customer answers, NLP may be taught.
2. Analyze text data
For anything from monitoring bad reviews to staying on top of the newest social media trends, natural language processing (NLP) enables businesses to translate textual data scattered over the web into actionable business insights and stay one step ahead of the competition.
3. Discover patterns
Topic modeling approaches may be used to detect consumer trends or to uncover hidden patterns in vast quantities of unstructured text, such as emails, customer reviews, social media profiles, or job applications, by using machine learning techniques.
4. Save Time and Money
Profitability is essential for every firm. And if you can figure out a means to save money, it will make the money-making element that much simpler. NLP is a proven method of saving both time and money for organizations across a wide range of sectors.
5. Increase conversions
Many marketing executives place a high priority on converting website visitors into paying clients. And they use a variety of techniques to increase conversion rates since the higher the conversion rate, the cheaper the cost of acquiring a new client.
Conclusion
The market for NLP training is growing at a fast pace, and it seems likely that its days are numbered. Is there, nevertheless, any hope for the future of NLP? In a nutshell, the answer is yes. Working by industry best practices, natural language processing consulting specialists will assist you in identifying the most appropriate strategy to address your business challenge and bring your ideas to fruition. There are benefits to each of the users out there. They can develop enterprise-level solutions for artificial intelligence, natural language processing, and speech-based technologies, such as voice transcription, sentiment analysis, and Chabot’s.
