Natural Language Processing (NLP)It works all around us and makes our life easier all the time, but we don't think about it all the time. From text recognition to data analysis,NLP applicationsthey are an integral part of our daily life.
NLP also benefits your business. This helps save time on monotonous, low-skilled tasks and can help reduce costs. This means you can invest more effort and resources into improving your products, processes and benefits.
In this part, we'll dive deeper into what NLP is, walk you through a set of natural language processing examples, and show you how to apply them in your organization.
Jump to the section that interests you the most here:
- Was ist Natural Language Processing?
- Examples of natural language processing
- the case
Was ist Natural Language Processing?
NLP (Natural Language Processing)is an artificial intelligence technique that enables machines to process and understand language like humans by using computational linguistics in combination with machine learning, deep learning, and statistical modeling.
Through NLP, computers not only understand the meaning, but also the mood and intent. Then they learn as they work, storing information and context to strengthen their future responses.
NLP can bring manyBenefits for your company. If you don't embrace NLP technology, you're likely missing out on opportunities to automate or gain business insights. This, in turn, can cause you to lose sales and growth.
You also risk falling behind your competitors.IBM Global Adoption IndexHe cited that almost half of the companies surveyed worldwide use some form of NLP-based application.
NLP is not perfect, mainly due to the ambiguity of human language. However, it has come a long way and without it many things, such as efficient large-scale analysis, would not be possible.
Examples of natural language processing that every company should know about
Natural language processing is evolving rapidly and its applications are evolving every day. This is great news for businesses as NLP can have a dramatic impact on the way you run your day to day business. You can speed up your processes, reduce the monotonous tasks of your employees and even improve relationships with your customers.
Ready to dive into some examples?
Here is a list of the top 10 natural language processing examples:
- translation of languages
- search engine results
- intelligent assistants
- Customer Service Automation
- research analysis
- Social Media Monitoring
- text analysis
- prediction text
1. Translation of languages
Thanks to natural language processing, online translators are now powerful tools. But it wasn't always like that. For example, if you remember the early days of Google Translate, you will remember that it was only used for word-for-word translations. It was unreliable to translate entire sentences, let alone text.
But now you can also easily translate grammatically complex sentences. This is mainly due to NLP in combination with the ability to “deep learn”. Deep learning is a branch of machine learning that helps decipher user intentions, words, and sentences.
2. Search Engine Results
Search engines no longer use just keywords to help users reach their search results. Now they analyze people's intent when they search for information using NLP. Context also allows them to improve the results they deliver.
Search autocomplete is a good example of how NLP works in a search engine. This feature predicts what you might be looking for so you can just click it and save yourself the trouble of typing it.
Smart Search is another tool powered by NPL that can be integrated with ecommerce search functionality. With every interaction, this tool learns the customer's intentions and offers corresponding results.
3. Smart assistants
Intelligent assistants, once science fiction, are now commonplace.
These smart assistants like Siri or Alexa use speech recognition to understand our everyday questions, and then use natural language generation (a subset of NLP) to answer those questions.
They are effectively trained by their owner and, like other NLP practitioners, learn from experience to provide better and more personalized support.
4. Customer Service Automation
Especially in growth phases, customer service costs a lot of time and money. Finding ways to combat this through automation is crucial.
Chatbots might be the first thing that comes to mind (we'll cover that in more detail shortly). But there are actually other ways NLP can be used to automate customer service.
NLP can be usedRecognize sentiment and keywords in emails. These emails can be automatically answered or automatically assigned to the appropriate team. That means customer emails aren't lost and issues are resolved quickly.
Similarly, you can also automate support ticket routing or ensure the right request reaches the right team. This is done using NLPunderstand what the customer needsdepending on the language they use. This is combined with deep learning technology to perform the routing.
A spam filter is probably the best known and most established email filter application. Spam accounts for about 85% of all global email traffic worldwide, so these filters are essential.
But filters have also evolved to help people organize their inboxes. For example, in Gmail, your emails can be categorized into Main, Social, Promotions, and Updates.
NLP works behind all these filters. When your emails arrive in your inbox, they are automatically scanned as welltext classificationmiKeyword ExtractionTools powered by NLP technology.
6. Research Analysis
When sending out surveys, whether to customers, employees, or any other group, you need to be able to do thisExtract actionable insights from datacome back
However, large amounts of information often cannot be analyzed manually. This is where natural language processing comes into playsentiment analysismiFeedback-AnalyseTools that search text for positive, negative, or neutral emotions.
MonoAprenderis a good example of a tool that uses NLP and machine learning to analyze search results. Can separate large quantitiesunstructured datato give you information in seconds.
Here is an example of an analysis performed by MonkeyLearn:
Chatbots are a great way to efficiently manage your customer service requests while freeing up your human team. They are immediately available 24/7, meaning your customers may not have to wait for an agent to be ready. At the same time, it helps to reduce costs. Win, win.
All of this would not be possible without NLP, which allows chatbots to listen to what customers are saying and provide an appropriate response. This reaction is amplified when mood analysis andintent assessmenttools are used.
8. Social Media Monitoring
People use social networks to communicate, be it to read and listen or to speak and be heard. As a business or brand, you can learn a lot about how your customers feel from what they comment, post, or hear.
However, trying to track down these myriad threads and piece them together into meaningful information can be challenging.
The solution? More precisely with NLPSentiment analysis tools like MonkeyLearnto be aware of how customers are feeling. You can then be notified of any problems that arise and fix them as soon as they occur.
9. Text Analysis
Companies today have to process huge amounts of unstructured text and data. Manually organizing and analyzing this data is inefficient, subjective and often impossible due to the volume.
NLP is special because it has the ability to understand these vast amounts of unstructured information. Tools like Keyword Extractors, Sentiment Analysis and Intent Classifier are particularly helpful, to name a few.
These NLP-based tools can review huge amounts of data from surveys, social media, emails, etc. and provide detailed analysis in seconds.
Also tools like MonkeyLearnInteractive studio dashboard(see below) and allows you to view your analytics in one place - click the link above to play our live public demo.
10. Prediction text
Text prediction is so ingrained in our daily lives that we don't always think about what's going on behind the scenes. As the name suggests, text prediction works by predicting what you are about to type. Over time, the word recognizer learns from you and the language you use to create a personal dictionary.
Predictive text and its autocorrect cousin have come a long way, and now we have apps like Grammarly that are powered by natural language processing and machine learning. We also have Gmail Smart Compose, which finishes your sentences as you type.
These are the most common natural language processing examples you're likely to encounter in your everyday life, and the most useful for your customer service teams.
In order to simplify certain areas of your business and reduce intensive manual work, it is imperative to harness the power of artificial intelligence.
However, since you are most likely dealing with humans, your technology needs to speak the same language as them. The introduction of natural language processing is mandatory.
MonoAprendercan help you create your own natural language processing models using techniques such as keyword extraction and sentiment analysis. which you can apply in different areas of your company.
Request your free demoLearn today how you can optimize your business with Natural Language Processing and MonkeyLearn.