Introduction
A computer could be deemed to be classified as intelligent if it was able to fool a person into thinking that it was a human.
There are many fascinating ideas, such as Turing Machines or the Pascaline, Bernstein mentions machine translation as one of the jobs computers can accomplish. Imagine for a moment that a computer is capable of comprehending the language of a text , and possessing the expertise in order to convert it into a different language.
Are machines capable of understanding text in the same way that we humans can? It's difficult to believe. However, for a while, it was magical to me, until I learned about the field of natural process of language (NLP) through data collection, a discipline that addresses this type of problem.
In this guide , I'll explain the subject and attempt to provide a basic understanding of some common applications of NLP. This guide is not intended to give a thorough explanation nor do I intend to introduce you to techniques and algorithms. This is merely an overview of the field which has been fascinating me for many years.
Applications Of NLP
NLP is all around and is everywhere, even if we aren't aware of it. While the term isn't as well-known like Big Data or Machine Learning however, we are using NLP all the time. Here are a few illustrations of ways NLP is used extensively:
Machine translation
Perhaps you've already tried machine translation and it's like a normal feature in the present. It is the globe icon on Twitter or the translate hyperlinks in Facebook posts, as well as in Google and Bing results for searches, or in some forums or user reviews systems.
Machine translation can be very effective in limited domains, which is, when the vocabulary and spoken words are widely used. For instance, it can drastically reduce the cost for translating tech manuals and support materials or catalogues that are specific to.
Thanks to advances in NLP in the past few years, machine translation is getting more precise. Today, computers can recognize text in images and provide translations.
Automated summary
Along with machines that translate, automatic summarization was introduced by the late 1950s. When a text is read it is the aim to create a simplified version that preserves the most essential details. Summary can be made through extracting and abstract.
An extraction-based strategy will find the most significant parts of text input typically sentences, and then be able to extract them in order to construct an overview. Apart from the challenge of determining the importance of every sentence an extraction-based summarizer must also have to consider coherence. For instance that a sentence in the summary may be used to refer to elements of a sentence not part of the summary. This is known as dangling aphora.
However abstraction-based approaches imply the generation of text The summarizer doesn't duplicate text from input, but composes in its own words that it has learned in the context of text. This is extremely complicated and at present, most of the software available employs an extraction-based method.
Analysis of sentiment
The purpose for sensual analysis is to detect untrue information in text. It could be a judgement of opinion, a view or even an expression of emotion and is an important problem for businesses and celebrities who want to know about their social media reputation. What do people think of our products? How do they feel of my restaurant or hotel? Are they pleased with our support for customers? What are their opinions about our competitors?
The most popular type of analysis that is performed on sentiment is known as polarity detection. which is the process of determining if an article on a certain subject is neutral, positive or negative. It may seem easy but in some instances it's
Here are few takeaways that you can apply to your enterprise adoption for AI Training.
1. No need of large amounts of data to start AI Project.
It’s a common misconception that any organization has to have a large amount of data that’s ready to go before starting your adoption process. While data readiness is helpful, there are still steps you can take to get going.
“There are very easy ways you can get introduced to artificial intelligence today starting with Audio Dataset or video… You don’t need large amounts of data to use artificial intelligence. You can just use the voice of your customer to start transcribing and analyzing what they’re saying.”
2. No need of Expert degree to derive customer insights.
It’s still useful to have some technical expertise to help connect some of the different tools used to analyze data, but these tools are becoming simpler to use. The different platforms and tools that are out there are making it easier to understand your customers.
For example, there are pre-trained machine learning models like natural language processing (NLP) that allow you to gain insights into things you may not have known existed. So according to Frantzen, “You don’t necessarily need to train your AI solution for things you’re looking for.”
3. Data readiness isn’t out of reach.
Even though AI tech is still in its early days, you don’t need to wait around another five years to start your investment.
“It takes time to train an AI solution, but the marketplace has come a long way to provide and facilitate that transition and allow people to start"
He recommends following this cognitive journey outline below.
- Start with an education of what AI is
- Learn about use cases where AI has been successful
- Start small and figure out where value can be added with existing technology
- Design a strategy and roadmap
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