Natural language processing
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Technology can take a lot of forms and NLP uses data to ‘understand’ natural language as humans do!
Alexa what is NLP?
Technology can take a lot of forms and has been integrated in our everyday lives. One of these technologies is natural language processing (NLP) which is a part of Αrtificial Ιntelligence (AI). Initially, it provides computers the ability to understand text and spoken words. NLP tends to understand natural language as humans do in the form of text or voice data and to ‘understand’ its full meaning, using a program that processes input and converts it to code.
So basically, NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly. You have probably interacted with NLP in the form of GPS systems, digital assistants and chatbots. NLP plays an important role in businesses nowadays, by simplifying the processes, can increase employee productivity and deal with heavy data.
So does this means that NLP systems will replace human employees?
This is an unlikely scenario – NLP will mostly automate tasks, will make better decisions and even upskill the labor. These new elevated tools will reorganize many roles in businesses, specially those of programmers. In this arena of constant technological development, organizations should compete about who will adopt faster the latest AI tech. Remember, a company that doesn’t follow these technological innovations is doom to fail and the managers who do not adapt are meant to be replaced!
Where do we use this nlp anyway?
By the time you open any electronic device, you can detect the many ways this AI tech is used. You only need to train your eyes in order to find them! The latest models that are being used are taking a step towards human level generalization. Which means that everyone can use these tools and organizations better start preparing to integrate them not only for their financial benefit, but also for its benefits in employee productivity.
Here are some examples of NLP in use:
Do you suffer from spam? Not anymore! Email filters are initial applications of NLP; it started out with spam filters, detecting certain words or phrases that signal a spam message. One example is when the system recognizes if emails belong in one of three categories (primary, social, or promotions) based on their contents. That helps the users to keep their inbox to a manageable size with easy access to the important and relevant emails.
Smart assistants become more and more mainstream and a part of many peoples homes. Having a smart assistant is starting to become a necessity rather than a luxury. There are plenty of them available but the most famous amongst them are Apple’s Siri and Amazon’s Alexa that recognize patterns in speech thanks to voice recognition and provide a useful response. We’re getting used to having conversations with Siri or Alexa throughout our homes with items like the thermostat, light switches, car and more. We now expect assistants to understand what we ask them, as they improve our lives and make certain activities easier.
Search engines use NLP to show results based on search behaviors. For example, search engines can predict what popular searches may be, but it can also recognize what you’re trying to say. Someone could put a half a sentence or a number and the search engine will show the anticipated results. These are some variations you may see when completing a search as NLP in search, associates the ambiguous query to a relative entity and provides useful results.
Someone said that people do not need to know the correct way a word is written because our phones are doing it for us. It is true that things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. Autocomplete and predictive text are similar to search engines predicting what the user is about to say based on what you type, finishing the word or suggesting a relevant one. And autocorrect will sometimes even change words so that the overall message makes more sense. Predictive text will customize itself to your personal language quirks the longer you use it!
Many languages don’t allow for straight translation and have different orders for sentence structure, which used to be a problem for translation services. But they’ve come a long way, online translators can translate more accurately and present grammatically–correct results. This is infinitely helpful when trying to communicate with someone in another language.
Digital phone calls
Do you ever wonder how often you have heard “this call may be recorded for training purposes’ ‘ when you contact a business customer service? These recordings go into the database for an NLP system to learn and improve in the future. Many telecommunication companies use automated systems in order to direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information.
Applying language to investigate data not only enhances the level of accessibility, but lowers the barrier to analytics across organizations, beyond the expected community of analysts and software developers. This opens up more opportunities for people to explore their data using natural language statements made up of several keywords that can be interpreted.
Text analytics converts unstructured text data into meaningful data for analysis using different linguistic, statistical, and machine learning techniques. Using NLP will scour customer interactions, such as social media comments or reviews, or even brand name mentions to see what’s being said. This use of NLP helps brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience. While a human touch is important for communications, NLP will improve our lives by managing and automating smaller tasks and then complex ones with technology innovation.
Natural Language Processing (NLP) Challenges
Have you ever tried to communicate with a chatbot or a virtual assistant but fail miserably? You desperately think what complicated thing did you just said and why this robot cannot understand you? The sad truth is that no matter how advanced modern AI has been, it will inevitably face some challenges. Those problems are closely linked to our language and speech methods; NLP also faces other issues that can hold back its development. Here are some of the challenges that Natural Language is facing:
Irony and sarcasm
Errors in text and speech
Lack of research and development
Python and the Natural Language Toolkit (NLTK)
The Python programming language provides a wide range of tools and libraries for specific NLP tasks. Many of these are found in the Natural Language Toolkit an open source collection of libraries, programs, and education resources for building NLP programs.The NLTK includes libraries such as sentence parsing, word segmentation, and tokenization ( breaking phrases, sentences, paragraphs into tokens that help the computer better understand the text).
Statistical NLP, machine learning, deep learning
The earliest NLP applications were hand–coded and could perform certain NLP tasks, but couldn’t easily scale to the increasing volumes of text and voice data.For example, statistical NLP combines computer algorithms with machine learning and deep learning models to automatically extract elements of text and then assign a statistical possibility.
NLP IS HERE TO STAY
It is obvious even to a skeptic that these technologies are not going anywhere any time soon. To begin preparing now, start understanding your text data and involve everyone in new roles. By adopting these new technologies you will add value to your firm, you will also be able to realize what works and what doesn’t and eventually let your employees adjust to the new reality. NLP and essentially AI have become an integrated factor for modern businesses and it will only grow bigger. While it has its limitations, it still offers huge and wide-ranging benefits to any business. Furthermore, it will further transform organizations and processes in this era of digital transformation and artificial intelligence.