Natural Language Processing (NLP) in the Telecommunications Industry

Natural Language Processing (NLP) in the Telecommunications Industry

Konrad Fulawka - April 27, 2022

The telecommunications industry is rapidly evolving. Operators are looking for ways to increase revenue and profits, while at the same time reducing costs. In this competitive environment, natural language processing (NLP) can be a valuable tool. NLP can help telecom service providers improve customer service, make better use of data, and reduce expenses. In this blog post, we will discuss the benefits of using NLP in the telecommunications industry and how to approach AI implementations.

Natural Language Processing – What Is It?

Natural language processing is a branch of Artificial Intelligence that allows computers to deal with textual data. Code is a natural language for computers, so naturally, they cannot understand Chinese or Polish unless they’re broken down with the appropriate techniques. Otherwise, it’s all Dutch to them!

NLP developers enable the machines to decipher the structure of language by creating models which can separate and comprehend details from natural language, both in the form of text and speech. Acting as a bridge, NLP enables the user to be understood by the computer without changing their speaking habits and figuring out how the machine “thinks.”

Natural language understanding NLU vs natural language generation NLG

Natural language understanding NLU vs. natural language generation NLG

How does it work in practice? In fact, the learning process in NLP quite resembles the way we humans learn a new language. However, the machines may need a little help from the developer’s side. The teaching process divides into morphological, syntactic, semantic, discourse, and pragmatic analysis and involves the following steps:

  • segmentation (breaking the textual or recorded data into constituent sentences and explaining them individually to our algorithm)
  • tokenizing  (breaking down the sentence into constituent words)
  • removing stop words that do not carry a particular meaning, like prepositions or verb forms
  • stemming (explaining to the machine the roots of the word and prefixes and suffixes)
  • lemmatization (identifying the base words for different word tenses, mood, gender, etc.)
  • part-of-speech tagging (explaining the concept of nouns, verbs, prepositions by adding them as tags to the words in our form)

Existing Models for NLP Tasks

NLP is everywhere – take the autocorrect or online translators for different languages used by most daily. But today, we’d like to focus on the business applications of natural language processing and, more specifically, on how the telecom industry uses it to optimize its processes.

Various levels of complexity of NLP tasks - from spell-checking, named entity recognition, and text classification through closed domain conversational agents and speech recognition to machine translation and open domain chatbots

Various levels of complexity of NLP tasks - from spell-checking, named entity recognition, and text classification through closed domain conversational agents and speech recognition to machine translation and open domain chatbots

Tasks like voice recognition, text classification, and entity extraction are commonly performed by NLP models in the telecom sector.

Meanwhile, if you’d like to dive deep into the topic of NLP in a broader context, here’s our take on this fascinating technology.

Telecom Industry-Specific Language

NLP in the telecom industry refers to the ability of machines to interact with humans using natural language that is very specific to this domain. In the telecommunications industry, there are a lot of jargon and abbreviations. 4G, 5G, UE(User Equipment), eNB, and BTS(Base Station) are just some examples. This can be confusing for customers and laymen, and it can also lead to miscommunication between humans and machines if the NLP model was trained on generic language datasets. NLP models need to be fine-tuned to telecoms-specific language in order to accurately understand particular needs.

The approach used within this industry goes even one level deeper. NLP algorithms are trained on large telecom-specific datasets which cover very specific parts and use cases of telecom-related terms and phrases. These datasets help machine learning algorithms learn the specific vocabulary and jargon used within narrow subsectors of the telecom industry. Thanks to that, AI applications trained on domain-specific data sets gain higher quality and overall better performance.

Telecoms-specific datasets and machine learning model training

Telecoms-specific datasets and machine learning model training

NLP in Telco - Case Studies for Improved Customer Experience

The telecommunication sector has embraced this branch of AI in its many forms (read more here), forging the path for NLP-based tools that were later swallowed by other sectors. Just like the banks and insurance companies, the telecom enterprises do not have many opportunities to stand out in the market with their offer due to the specifics of the industry. As a result, they have to search for other ways to win the battle for customers.

Polishing the customer experience is their most common way to conquer the clients. And since the nature of telecom services creates demand for loads of professional support and interactions between the customer and the company, it becomes essential to optimize this area. NLP brings it to a whole new level, allowing telecom businesses to solve the typical customer problems in an efficient manner and wherever possible without human intervention.

Before we move on to the NLP use cases, maybe you would like to read more about AI in telecom? If so, we recommend checking our blog series on this topic.

In the meantime, let’s look into NLP applications for customer service.

NLP use cases in the telecom industry supporting customer experience

Virtual Assistants

There are cases in which the support of a real-life consultant is worth its weight in gold. However, for the most part, virtual assistants can handle customer problems impressively well (and fast!). Many companies have appreciated their potential, going all in, like Walmart’s Sam’s Club, where 90% of the questions are answered by an automated bot.

Every extra minute of waiting makes the CSI drop sharply, and with virtual assistants powered by natural language processing, the companies can finally meet the expectations of their clients in that field. Bots can solve their issues fast and 24/7, cutting the customer service costs.

Whether they’re chatbots or voice bots, the rule is the same – they identify the relevant information from the written or spoken inquiries and enter in dialogue with them. In order to be able to solve more complex customer problems and learn with every interaction, it’s worth using deep learning techniques. Neural networks enable the bots to mimic human behavior, making the whole experience feel more natural for the user.

Good training is crucial to avoid the “dumb bot” issue that frustrates the customers so much and stops the companies from reducing costs – because the bot remains just an additional stopover instead of actually solving problems. If the NLP is implemented thoughtfully with the support of machine learning models, the bot will be able to identify intent almost like a human, taking over a great deal of the consultant’s work.

Categorizing Support Requests

Big telecom companies deal with hundreds of thousands of support requests every day. With an omnichannel approach, their processing becomes quite challenging. Every request needs to be assigned to a specialist that can solve a particular problem.

With NLP, this process becomes much simpler and faster. The support agents do not have to look through every request manually to be able to direct it to an authorized person - the algorithm auto-tags and categorizes it (so-called taxonomy). Since the model is trained to recognize the root of the issue by associating particular words with particular problems, it can assign the support request automatically (auto-routing).

Telecom companies can also use NLP for prioritizing requests. This feature helps to organize the tasks in a customer-friendly and efficient way while facilitating the optimization of the processes simultaneously. Having the categorized requests at their disposal, the analysts can find the most frequent issues that sabotage a company’s business growth and affect the customer experience in a negative way. The conclusions can later serve for prioritizing innovations.

Resolving Support Tickets

With NLP, the companies can not only categorize but also resolve tickets. They save on customer service this way, but more importantly, they provide the professionals with time and space to focus on more complicated issues.

After finding the relevant information in the support request, the trained algorithm can categorize it and decide whether it should be assigned to a particular specialist right away or rather solved in an automated manner. For instance, it could send ready-made responses after identifying the root of an issue or redirect the user to already existing resources on the company’s website.

The support agents can also take advantage of NLP through template response suggestions. In this case, the model suggests the answer by drawing from an extensive library of information from chats and emails within the identified category. It’s an excellent way for the enterprise to improve response time.

AI in Call Centers

Even if the customers think otherwise, their problems are predictable and repeatable to a certain extent. That creates cost-cutting opportunities for the telecom companies when managing their omnichannel tickets but also allows them to revolutionize their call centers.

With NLP, and more specifically, voice recognition and voice assistance techniques, a great deal of work that used to be carried out by the call center agent can be automatized. As customers, we’re already used to the systems where we interact with the recordings to get redirected to an appropriate department and reach the agent that will solve our issue. Based on traditional rule-based programming, they help to assign and prioritize the calls but do not solve problems. Machine learning combined the NLP can make these interactions much more sophisticated and less limiting for the client.

So far, just a tiny fraction of the telecom companies with their own call centers can boast of the system that brings this experience close to a real-life conversation. The voice assistants often respond in a non-relevant way. That’s why improving the model’s ability to detect intent is a priority for telecom companies at this point.

If you’re struggling with similar issues, don’t hesitate to contact us. We can help you elevate the response accuracy and solve other issues like the poor understanding of different accents.

NLP Sentiment Analysis

Let’s move to another great feature NLP provides companies with. In a nutshell, sentiment analysis is an automated recognition of positive and negative feedback received from B2B or B2C customers.

While in the case of categorization, the model is taught to associate particular words with the root of the issue the customer comes up with. Here, it links the expressions with positive or negative sentiments, marking the feedback accordingly. Sentiment analysis is a helpful tool for the Customer Satisfaction Specialists or Loyalty Managers to find areas for improvement in customer service/customer support departments.

Competitor Analysis

Aside from sentiment analysis, the NLP offers another analytics tool that can support a company’s growth - not only in the telecom sector. To evaluate the risk of introducing a new service/telecom product to the market, the companies need to know everything about their competition. The AI model can scan the content of the mails or voice recordings and look for the competitor names, tagging context and categorizing the mentions of competitors.

Having this data extracted, the companies can identify good ad bad practices, optimizing their sales and marketing strategies. AI looks for competitor names mentioned in emails or calls, tags context, and categorizes mentions. Aside from that, the analysts also use NLP to streamline the preparation of competitor analysis and validate it. The model can look through vectorized descriptions of companies extracted from freely available resources (like or Crunchbase) and point out the most relevant competitors with the support of NLP.

Case Studies for Streamlined Network Operations for Telecom Service Providers

Just as it does with customer service, the NLP can also serve for streamlining network operations. No wonder the telecommunications sector has embraced it to that extent! Here are some examples of how helpful natural language processing can be in other areas than customer support.

NLP use cases for network optimization and streamlined telecoms network operations

Automated Infrastructure Documentation Generation

Maintaining a clean and organized infrastructure documentation and keeping it updated is essential for telecom operators. In the case of any network failure, it is necessary to look through it in search of an issue. If the language is too complicated or the use of terminology is not consistent, finding relevant information is extremely challenging, particularly in the case of extensive networks.

With NLP, companies can partially automate the generation of the critical infrastructure documentation, standardizing the documents, creating templates, and automatic suggestions that help keep the writing as clear as possible. The NLP engine can verify the usage of terminology and suggest expressions. As a result, maintaining clean and updated documentation becomes much easier, allowing the network support to fix the recurring issues faster and streamlining information flow as well as onboarding and network optimization.

Data Mining for Telecommunications Network Log Analysis

With the environments becoming increasingly distributed, managing logs and resource utilization becomes more challenging than ever. And as with the business growth, the log volume has to increase, making its management as efficient as possible becomes a priority. Troubleshooting the logs is a time-consuming and labor-intensive process, and that, of course, means costs.

NLP can streamline that process, making it much easier and faster to detect issues in real-time and solve them. Since the logs usually contain written text, the developers can use NLP to classify and group them. Such structuring facilitates extracting relevant information concerning the issues.

Using Natural Language to Configure a Network Management System (NMS)

Using NLP, telecom companies can empower their non-technical employees while avoiding costly training at the same time. With the support of the machine trained to interpret the natural language requests, they can configure a network management system without technical skills, just via speech.

The exact course of that process may depend on the developers’ approach, but without going into detail – the NLP engine extracts the information from the command and converts it into a structured network request (SNR) that later gets translated and validated by the deep learning network. The process ends up with the translated SNR being processed by the network management system via API. Result? Fast and human-friendly configuration, less frustration and errors.

Benefits of Applying Natural Language Processing Solutions in the Telecom Industry

The examples above prove that natural language processing creates immense possibilities for telecom companies, helping them streamline customer service as well as network operations.

As a result of implementing natural language processing, the telecom enterprises:

  • improve the customer experience and CSI - the customers get their problems solved faster and at any time;
  • cut costs on customer service/support - a part of the responsibilities is taken over by the AI, reducing the workforce demand and offloading the consultants
  • find the areas for improvement easier - sentiment analysis and competitor analysis with NLP provides them with a holistic overview
  • simplify and partially automate a complex ticketing system - the employees can focus on more complex issues while the AI takes care of the typical ones

Benefits of NLP in the telecommunication industry

The Future of Machine Learning and NLP in the Telecommunication Industry

As for today, the developers designing solutions for the telecommunication industry focus on improving the machine’s comprehending capabilities, trying to get as close to the human way of processing language as possible. That requires some advance in detecting sentiments or humor and solving accent-related issues.

The increasing popularity of virtual assistants based on emerging technologies such as Alexa, Siri, or Google Assistant proves how far we’ve got in the development of natural language processing. NLP-based solutions are becoming an essential part of our daily life, and the telecom companies will be embracing that – also for their own good!

Our teams at nexocode apply natural language processing techniques in various projects. If you’re curious about these, check our NLP development services and solutions page and our case studies documenting NLP and robotic process automation implementations for a social platform for idea-exchange, an AI-based mental health support app, or a corporate booking platform.

And if you’d like to check the potential of NLP for your own project, drop us a line!

AI Design Sprint on NLP

Join our AI Design Sprint workshops focused on NLP to design your next AI-based solution.

About the author

Konrad Fulawka

Konrad Fulawka

Strategic Advisor and Telco Expert

Linkedin profile

Konrad Fulawka graduated from the University of Technology in Wroclaw and has almost 20 years of experience in the Telecommunications Industry.
For the last 11 years, he works for Nokia. Over the time, Konrad was responsible for leading international and multicultural teams working on many complex telecommunication projects, delivering high-quality software worldwide. During the last few years, he is heading the Nokia Garage - Innovation Hub, which helps Nokia drive cutting-edge innovative projects.
At nexocode, Konrad acts as a strategic advisor and Telco Expert with unparalleled insight into global business trends and best practices across all verticals. He loves DIY (Do It Yourself) activities besides Political Economy and Financial Services Markets.

Would you like to discuss AI opportunities in your business?

Let us know and Dorota will arrange a call with our experts.

Dorota Owczarek
Dorota Owczarek
AI Product Lead

Thanks for the message!

We'll do our best to get back to you
as soon as possible.

This article is a part of

AI in Telecommunications
7 articles

AI in Telecommunications

Artificial Intelligence and ML are disrupting and transforming telecom businesses. Telecommunications companies can leverage these technologies to improve customer retention, enable self-service, improve equipment maintenance, and allow for an undisrupted flow of the evergrowing amounts of telecom data.

These advancements will also reduce operational costs, which means you're likely going see more savings than ever before! Click here for our article series about how AI revolutionizes the Telco industry across all areas.

check it out


Insights on practical AI applications just one click away

Sign up for our newsletter and don't miss out on the latest insights, trends and innovations from this sector.


Thanks for joining the newsletter

Check your inbox for the confirmation email & enjoy the read!

This site uses cookies for analytical purposes.

Accept Privacy Policy

In the interests of your safety and to implement the principle of lawful, reliable and transparent processing of your personal data when using our services, we developed this document called the Privacy Policy. This document regulates the processing and protection of Users’ personal data in connection with their use of the Website and has been prepared by Nexocode.

To ensure the protection of Users' personal data, Nexocode applies appropriate organizational and technical solutions to prevent privacy breaches. Nexocode implements measures to ensure security at the level which ensures compliance with applicable Polish and European laws such as:

  1. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (published in the Official Journal of the European Union L 119, p 1); Act of 10 May 2018 on personal data protection (published in the Journal of Laws of 2018, item 1000);
  2. Act of 18 July 2002 on providing services by electronic means;
  3. Telecommunications Law of 16 July 2004.

The Website is secured by the SSL protocol, which provides secure data transmission on the Internet.

1. Definitions

  1. User – a person that uses the Website, i.e. a natural person with full legal capacity, a legal person, or an organizational unit which is not a legal person to which specific provisions grant legal capacity.
  2. Nexocode – NEXOCODE sp. z o.o. with its registered office in Kraków, ul. Wadowicka 7, 30-347 Kraków, entered into the Register of Entrepreneurs of the National Court Register kept by the District Court for Kraków-Śródmieście in Kraków, 11th Commercial Department of the National Court Register, under the KRS number: 0000686992, NIP: 6762533324.
  3. Website – website run by Nexocode, at the URL: whose content is available to authorized persons.
  4. Cookies – small files saved by the server on the User's computer, which the server can read when when the website is accessed from the computer.
  5. SSL protocol – a special standard for transmitting data on the Internet which unlike ordinary methods of data transmission encrypts data transmission.
  6. System log – the information that the User's computer transmits to the server which may contain various data (e.g. the user’s IP number), allowing to determine the approximate location where the connection came from.
  7. IP address – individual number which is usually assigned to every computer connected to the Internet. The IP number can be permanently associated with the computer (static) or assigned to a given connection (dynamic).
  8. GDPR – Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals regarding the processing of personal data and onthe free transmission of such data, repealing Directive 95/46 / EC (General Data Protection Regulation).
  9. Personal data – information about an identified or identifiable natural person ("data subject"). An identifiable natural person is a person who can be directly or indirectly identified, in particular on the basis of identifiers such as name, identification number, location data, online identifiers or one or more specific factors determining the physical, physiological, genetic, mental, economic, cultural or social identity of a natural person.
  10. Processing – any operations performed on personal data, such as collecting, recording, storing, developing, modifying, sharing, and deleting, especially when performed in IT systems.

2. Cookies

The Website is secured by the SSL protocol, which provides secure data transmission on the Internet. The Website, in accordance with art. 173 of the Telecommunications Act of 16 July 2004 of the Republic of Poland, uses Cookies, i.e. data, in particular text files, stored on the User's end device.
Cookies are used to:

  1. improve user experience and facilitate navigation on the site;
  2. help to identify returning Users who access the website using the device on which Cookies were saved;
  3. creating statistics which help to understand how the Users use websites, which allows to improve their structure and content;
  4. adjusting the content of the Website pages to specific User’s preferences and optimizing the websites website experience to the each User's individual needs.

Cookies usually contain the name of the website from which they originate, their storage time on the end device and a unique number. On our Website, we use the following types of Cookies:

  • "Session" – cookie files stored on the User's end device until the Uses logs out, leaves the website or turns off the web browser;
  • "Persistent" – cookie files stored on the User's end device for the time specified in the Cookie file parameters or until they are deleted by the User;
  • "Performance" – cookies used specifically for gathering data on how visitors use a website to measure the performance of a website;
  • "Strictly necessary" – essential for browsing the website and using its features, such as accessing secure areas of the site;
  • "Functional" – cookies enabling remembering the settings selected by the User and personalizing the User interface;
  • "First-party" – cookies stored by the Website;
  • "Third-party" – cookies derived from a website other than the Website;
  • "Facebook cookies" – You should read Facebook cookies policy:
  • "Other Google cookies" – Refer to Google cookie policy:

3. How System Logs work on the Website

User's activity on the Website, including the User’s Personal Data, is recorded in System Logs. The information collected in the Logs is processed primarily for purposes related to the provision of services, i.e. for the purposes of:

  • analytics – to improve the quality of services provided by us as part of the Website and adapt its functionalities to the needs of the Users. The legal basis for processing in this case is the legitimate interest of Nexocode consisting in analyzing Users' activities and their preferences;
  • fraud detection, identification and countering threats to stability and correct operation of the Website.

4. Cookie mechanism on the Website

Our site uses basic cookies that facilitate the use of its resources. Cookies contain useful information and are stored on the User's computer – our server can read them when connecting to this computer again. Most web browsers allow cookies to be stored on the User's end device by default. Each User can change their Cookie settings in the web browser settings menu: Google ChromeOpen the menu (click the three-dot icon in the upper right corner), Settings > Advanced. In the "Privacy and security" section, click the Content Settings button. In the "Cookies and site date" section you can change the following Cookie settings:

  • Deleting cookies,
  • Blocking cookies by default,
  • Default permission for cookies,
  • Saving Cookies and website data by default and clearing them when the browser is closed,
  • Specifying exceptions for Cookies for specific websites or domains

Internet Explorer 6.0 and 7.0
From the browser menu (upper right corner): Tools > Internet Options > Privacy, click the Sites button. Use the slider to set the desired level, confirm the change with the OK button.

Mozilla Firefox
browser menu: Tools > Options > Privacy and security. Activate the “Custom” field. From there, you can check a relevant field to decide whether or not to accept cookies.

Open the browser’s settings menu: Go to the Advanced section > Site Settings > Cookies and site data. From there, adjust the setting: Allow sites to save and read cookie data

In the Safari drop-down menu, select Preferences and click the Security icon.From there, select the desired security level in the "Accept cookies" area.

Disabling Cookies in your browser does not deprive you of access to the resources of the Website. Web browsers, by default, allow storing Cookies on the User's end device. Website Users can freely adjust cookie settings. The web browser allows you to delete cookies. It is also possible to automatically block cookies. Detailed information on this subject is provided in the help or documentation of the specific web browser used by the User. The User can decide not to receive Cookies by changing browser settings. However, disabling Cookies necessary for authentication, security or remembering User preferences may impact user experience, or even make the Website unusable.

5. Additional information

External links may be placed on the Website enabling Users to directly reach other website. Also, while using the Website, cookies may also be placed on the User’s device from other entities, in particular from third parties such as Google, in order to enable the use the functionalities of the Website integrated with these third parties. Each of such providers sets out the rules for the use of cookies in their privacy policy, so for security reasons we recommend that you read the privacy policy document before using these pages. We reserve the right to change this privacy policy at any time by publishing an updated version on our Website. After making the change, the privacy policy will be published on the page with a new date. For more information on the conditions of providing services, in particular the rules of using the Website, contracting, as well as the conditions of accessing content and using the Website, please refer to the the Website’s Terms and Conditions.

Nexocode Team