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NLP: What is it and what is it used for?

As we all know, language plays a critical role in our society. It is the medium for humans to exchange ideas and communicate their feelings with each other. Over the years, we have seen that medium expands into books, telephone conversations, songs, movies, emails, texts, and virtual assistants like Alexa or Siri. It’s a bit hard to imagine our world without language. 

This is why Natural Language Processing (NLP), one subsection of Artificial Intelligence and Machine Learning, has become one of the hottest topics in Data Science. 

Just think about how much we can learn from the text and voice data we encounter every day. In today’s world, this level of understanding can help improve both the quality of living for people from all walks of life and enhance the experiences businesses offer their customers through digital interactions.  

What is Natural Language Processing?

NLP is how machines understand and interpret human language. 

Natural language processing is important for businesses to make sense of large amounts of unstructured text, whether in email, social media conversations, online chats, survey responses, voice conversations, and many other forms of data. 

Let’s go over that again. NLP is the machine’s ability to process what was said, structure the information received, determine the necessary response, and respond in a language that we understand. So, how does NLP work, and what is NLP used for?

Why is NLP important?

Our language has so many variations and nuances that it can be pretty overwhelming for an individual or business to analyze. Every written or verbal expression adds some type of information that can be interpreted and value can be extracted from it. The problem is if you want to analyze a few thousand or million “conversations” — the job can be incredibly cumbersome. So, why is NLP important?

  1. To analyze large volumes of text or speech data
  2. To standardize a highly unstructured data pipeline

Today’s machines can analyze so much information – consistently and without fatigue. Ultimately, it comes down to training a machine to better communicate with humans and to scale the myriad of language-related tasks. 

How NLP Works

In order to create effective NLP models, you have to start with good quality data.

NLP business applications come in different forms and are so common these days. For example, spell checkers, online search, translators, voice assistants, spam filters, and autocorrect are all NLP applications.

By bringing NLP into the workplace, companies can tap into its powerful time-saving capabilities to give time back to their data teams. Now they can focus on analyzing data to find what’s relevant amidst the chaos, and gain valuable insights that help drive the right business decisions. 

Top 10 Applications of Natural Language Processing

What tasks can be solved with NLP? There are different natural language processing tasks that have direct real-world applications while some are used as subtasks to help solve larger problems. 

Let’s look at 10 of the most popular applications of natural language processing:

  1. Automatic Summarization
  2. Speech Recognition & Classification
  3. Sentiment Analysis
  4. Grammar Checks & Text Prediction
  5. Translating Language
  6. Chatbots & Voice Assistants
  7. Market Intelligence
  8. Text Classification
  9. Character Recognition
  10. Customer Support

 

There’s often not enough time to read all the articles your boss, family, and friends send over. Wouldn’t it be nice if there were tools like Sparknote, but for PDF’s? That’s where AI can help. 

This application helps extract the most important information from any given text document and provides a summary of that content. Its main goal is to simplify the process of sifting through vast amounts of data, such as scientific papers, news content, or legal documentation.

Tools like Meaning Cloud Summarization and Text Summary highlight the main points of an article, without you taking the time to dig through the clutter. If only we had this during school. 😬

Decoding speech and converting it to text has been a goal for scientists for years. With the recent progress in NLP, it has only now become possible at scale. 

The most obvious use cases for speech recognition are tools you probably use daily – Siri, Google Assistant, and Alexa. Although these tools aren’t perfect, they are best used while your hands are busy (driving, cooking, etc.) and will only improve with time. One of the main ways these virtual assistants are improving over time is through the assistance of humans, a form of Supervised Learning called Human in the Loop. You might have read that in 2019 the big players (talking about Google, Amazon, and Apple) have in fact analyzed user voice data using a network of human annotators to improve their virtual assistants. 

This isn’t to say that there aren’t other applications for Speech Recognition systems. For example, adding speech-to-text capabilities to business software, companies are able to automatically transcribe calls, send emails, and even translate. This is useful for Video Communication or Call Center focused technology. 

Machines are still pretty primitive – you provide an input and they provide an output. Humans are a little bit more complicated. Although they might say one set of words, their diction does not tell the whole story. We are emotionally intelligent, and use it in everyday life. 

Machines need human input to help understand when a customer is satisfied or upset, and when they might need immediate help. If machines can learn how to differentiate these emotions, they can get customers the help they need more quickly and improve their overall experience. 

Some industry leaders in sentiment analysis are MonkeyLearn and Repustate.

google search text prediction autocompleteTools like Google and Grammarly make emails and proposals easy and professional. With so much data on the internet on how to spell and write correctly, these machine learning tools are able to become extremely accurate. Google’s predictive speech saves you time and finds the words that are always lingering at the tip of your tongue.

As companies and individuals become increasingly globalized, effortless, and smooth communication is a business essential. Currently, more than 100 million people speak 12 different languages worldwide. Even if you hire a skilled translator, there’s a low chance they are able to negotiate deals across multiple countries. That’s where tools like Google Translate and Deep L come into play. In March of 2020, Google unveiled a new feature that allows you to have live conversations using Google Translate. If you want to learn more, here’s a demo video. With the power of machine learning and human training, language barriers will slowly fall.

Here at swivl.ai, we know a thing or two about chatbots. 😉  But seriously, when it comes to customer inquiries, there are a lot of questions that are asked over and over again. 

Corporations are always trying to automate repetitive tasks and focus on the service tickets that are more complicated. That is where chatbots and voice assistants can come into play. They can help filter, tag, and even answer FAQ’s (frequently asked questions) so your employees can focus on the more important service inquiries. 

In addition, there’s a significant difference between the rule-based chatbots and the more sophisticated Conversational AI. Now you can build a custom assistant that not only helps answer support questions but is ingrained within your customer’s digital journey to dynamically recommend content, products, and services contextual to that customer’s past history with your brand. 

Have you ever been scrolling through Instagram and all you can see are bot profiles trying to fill your screen? It’s annoying. 

However, the same technologies used for social media spamming can also be used for finding important information, like an email address or automatically connecting with a targeted list on LinkedIn. Marketers can benefit tremendously from natural language processing to gather more insights about their customers with each interaction. 

Services like Seamless AI, which finds leads’ contact information, and ML Analyzer, a service that summarizes a text, can save you time on research to focus a team’s energy on building relationships or making decisions.

The goal of text classification (also known as text categorization or text tagging) is to contextually assign text (that is primarily unstructured) in documents, emails, chats, social media, support tickets, surveys, or search – for example – into tags or categories according to its content. 

Examples include first and last names, age, geographic locations, addresses, product type, email addresses, company name, etc. Text classification has broad applicability such as social media analysis, sentiment analysis, spam filtering, and spam detection. 

Optical Character Recognition is extremely useful. OCR helps speed up repetitive tasks, like processing handwritten documents at scale. Legal documents, invoices, and letters are often best stored in the cloud, but not easily organized due to the handwritten element. Tools like Microsoft OneNote, PhotoScan, and Capture2Text facilitate the process using OCR software to convert images to text.

Have you ever needed to change your flight or cancel your credit card? Most of the time, there is a programmed answering machine on the other side. Although sometimes tedious, this allows corporations to filter customer information and quickly get you to the right representative. These machines also provide data for future conversations and improvements, so don’t be surprised if answering machines suddenly begin to answer all of your questions with a more human-like voice.

Get Started with NLP

Machine Learning and Natural Language Processing are more accessible than ever before, but the fact remains that AI can be complicated to implement. There are still many challenges that make Natural Language hard to crack – even the likes of Google and Apple have poured countless resources in trying to figure out. 

Businesses live in a world of limited time, limited data, and limited engineering resources.

No Code NLP Tools

Converting written or spoken human speech into an acceptable and understandable form can be time-consuming, especially when you are dealing with a large amount of text. To that point, Data Scientists typically spend 80% of their time on non-value-added tasks such as finding, cleaning, and annotating data. This process of cleaning (identifying and removing errors from data) and correctly labeling data (the act of accurately classifying similar objects into various categories) is critical to improving the quality of the training data being fed into the machine learning model. 

There are various Natural Language Processing tools out there that provide a friendly no-code user interface (point and click) and decrease an annotator’s workload in order to quickly train large amounts of messy data and help supervise the models they are working on. 

For example, swivlStudio allows you to visualize all of the utterances (what people say or ask) in one inbox. These are either tagged as Handled (your model was successful at generating a next step) or Unhandled (the model scored below a certain confidence threshold) so that you have a full visual as to how your model is performing.

 

The great thing about swivl.ai’s Training Inbox is that it allows you and your team, the people that know your business best, to continuously train and optimize your NLP model through Duolingo type exercises in order for your AI-powered bot to always perform at it’s best.  

 

 

Build or Buy?

It’s important to assess your options based on your employee and financial resources when making the Build vs. Buy Decision for a Natural Language Processing tool. A great NLP Suite will help you analyze the vast amount of text and interaction data currently untouched within your database and leverage it to improve outcomes, optimize costs, and deliver a better product and customer experience.

 

Have you talked to Hoover yet? If not, click on the floating orange owl and start chatting! There you can learn more about how our natural language processing tools can streamline your business!

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1 Comment

Top 10 Natural Language Processing (NLP) Applications – What’s trending
September 24, 2020

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