Natural Language Processing Services

The use of machine learning models in NLP enables computers to better understand human language. Natural language processing services help make this possible by annotating sequence fragments,  meaning/intent, or name entities so that algorithms can learn and generalize.

Natural language processing is the process which allows a computer program to understand language as it is normally spoken and written. I.e., instead of having to type in specific commands, you can just say or type what you want the computer to do. Virtual assistants use natural language processing.

It’s a subset of AI, and is used in business intelligence, search engines, medical research, and any number of other fields. The system processes data and uses algorithms to let it understand what you want to say and/or to generate natural language responses that are easily readable by humans.

How Natural Language Processing Works

One of the most important steps in natural language processing is annotation and labeling. This involves identifying and marking up the different parts of a text – such as individual words, phrases, and sentences. Annotating a text makes it much easier for the computer to understand, and it also helps to improve the accuracy of the results.

Once the text has been annotated, the next step is to feed it into a natural language processing algorithm. This will parse the text and break it down into its component parts. The algorithm will then analyze these parts and look for patterns and relationships between them.


The more data the algorithm processes, the better it becomes. Think about how you used to have to put in stilted, ungrammatical keywords when searching the internet, and now you can just type in “how does natural language processing work” and get an answer. It’s all machine learning, and each search teaches the algorithm more about what the searchers want. You can see how this might translate across to all kinds of things you might want to do with your business.

What happens next depends on the specific use case. The processed language might be used to sort the data, pull information from it, send you what they think is the right website, or target ads based on perceived interest. NLP can also be used to generate new text, which might be used to summarize findings or create a better chatbot. This is also used for machine translation, where the system processes and analyzes phrases in language A and then generates the text in language B. While this is not quite as good as human translation, it is far cheaper and is more than serviceable for websites and the like.

Typically, because of the processing power involved, NLP takes place in the cloud. This also allows the same algorithms to potentially be shared by multiple customers. Local processing tends to be particularly expensive and resource-heavy. That said, with the modern cloud and connectivity, there is no issue with lag and the system can be used from wherever you happen to be. However, some use cases do not guarantee access to the network (e.g. translation systems for coast guards who operate in places where network is not available). To the user, NLP should look seamless, especially in real time applications such as chatbots or translation.

NLP can be used to save a lot of human time. For example, it’s not humanly possible to keep up with all the news about an event these days, but an algorithm can do so and present you with a summary or consensus, or direct you to articles that are particularly relevant. They can be used to answer questions so that sales and customer service reps don’t have to do as much tedious work. And overall, as NLP matures, interacting with computers and other machines is going to become more intuitive and less like “work.”

NLP Use Cases

So, are you wondering if you “need” natural language processing in your business? There are a wide variety of use cases for NLP. Here are some to consider:

  1. Classifying e-mails and support tickets into categories and directing them to the right person. For example, this might allow complicated tech support questions to be automatically escalated, whilst simple ones can be answered by the AI without any human input. Sigma solutions classify emails based on content, subject and sender. E-mail can then be directed to the right person, prioritized, and/or labeled. An example might be if you have an email address on your website, the system can then automatically direct sales questions to sales, website tech support to IT, etc. Furthermore, it can apply rules to color code messages, so IT knows if the tech support request is urgent (your entire website is down) or minor (there’s a typo on page X).
  2. Providing intelligent agents to handle the majority of sales conversations. These systems “know” when to transfer the rest to human agents. This reduces customer wait times, helps customers find product and service information quickly, and increases sales. It does not replace salespeople, but rather ensures that they are not wasting their time on basic questions while customers sit on hold. Customers are typically happier to get the rapid service and real time responses, although some will always insist on dealing with a person. Intelligent agents can also call for a supervisor if needed.
  3. Extracting customer information from call center calls without the person needing to write it down. This can then be used to build databases that can be used to train agents and estimate the quality of leads. Individual agents could use the system as a substitute for making notes, and it makes it a lot less likely that addresses or names will be written down incorrectly.
  4. Analyze political debates to compare candidates and help people make a more informed choice when voting. These analyses have been used to support media coverage of elections, but could also be used by informational sites that want to provide, say, checklists of where candidates stand on specific issues.
  5. Monitoring product launch and other social media feedback. NLP can let you “read” all the tweets being made about you and your product and then provide you with a useful summary. It can work across multiple languages. If you have a major launch and are expecting a high volume of feedback, you can deploy the algorithm to detect, summarize, or alert you to what is being said. This is particularly important for software launches, where it seems inevitable that bugs will be found by users no matter how carefully you test.
  6. Classify, monitor and index relevant news. The AI can monitor all of the information about a specific matter and thus reduce the amount of time spent manually searching for and reading news pieces. Again, it can provide a summary, or a list of relevant articles so a human can decide which ones to investigate further. Oh, and it can include audio and audiovisual sources as well. You can use this to track things that might affect your business, such as politics along supply lines, weather, protests, etc.
  7. Detect fake information or hate speech automatically. NLP systems can avoid the “keyword problem” for moderation (where, for example, a harmless word can cause a user to be banned because the system did not understand the context). Natural language processing can save moderator time by curating feeds and establishing what needs to be shown to a human moderator.
  8. Identify and redact personally identifiable information so it is not shown to anyone who does not need to see it. This can protect customers and employees from data breaches.
  9. Analyze reviews and provide you with a summary of the good and bad things customers are saying, including specific issues. This can help you get what you need to know if customers are complaining on the internet instead of coming to you with the problem. Sentiment analysis can look at reviews, social media and notes taken by customer service representatives to work out exactly what people think about your brand and what you can improve.
  10. Analyze and categorize medical records to help with disease prediction and prevention, or analyze clinical trial data rapidly.

Results show that adopting NLP can result in improved customer experiences, deeper customer insights, and transformative business decisions. Even if your needs don’t match anything on this list, you might be able to acquire a custom solution which will solve a problem your business is having, or improve your customer service and sales. For example, imagine a system which alerts you only to bad reviews so you can have somebody see what the complaint is and address it. One which analyzes each mention of your company for actionable information. Or even one which tells you what is being said about you in languages you don’t speak.

Getting Started With Natural Language Processing can help you get started with NLP. The quality of your training data annotation has a direct impact on the effectiveness of your machine learning. We provide annotation services in over 250 languages and dialects. Since 2008, we’ve helped companies scale the development of conversational and speech technologies and products. This includes years of working with natural language processing algorithms and adapting them to specific real-world conditions. If you are interested in NLP for your business, contact us today.

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Sigma offers tailor-made solutions for data teams annotating large volumes of training data.