Unleashing the potential of Natural Language Processing

March 24, 2022 - Words by Álvaro García Faura, Jasna Simončič

March 24, 2022
Words by Álvaro García Faura, Jasna Simončič

Natural Language Processing (NLP) is one of the most promising AI subfields, revolutionizing business processes and unveiling opportunities across different sectors. From more efficient document processing and classification, to more advanced use cases such as automatic and seamless incident detection through real-time monitoring of system or application logs, every business can benefit from NLP.


What is NLP?

Natural Language Processing (NLP) is a broad field within Artificial Intelligence (AI) that, at its core, deals with enabling computers to process, interpret and understand human language. The popularity of NLP has soared in recent years, fueled by the research that has enabled the training of large language models (such as OpenAI’s GPT-3), and it is the democratization of these models that is bringing state-of-the-art NLP to businesses big and small. Here’s some of the things you can do with NLP.


Common ways in which businesses use NLP

Assistants and Chatbots

Virtual assistants such as Siri, Alexa or the Google Assistant have become widespread tools which we casually go to for things from setting up a timer for us while cooking to telling us bad jokes. But they can also be the perfect opportunity for local businesses to attract new customers and expand their online presence.

And then there’s chatbots. Regardless of how much we might hate them when we are desperately wanting to be addressed by a human interlocutor, the truth is that chatbots help increase customer support efficiency and, in turn, customer satisfaction.

Sentiment Analysis

Sentiment analysis is a subfield of NLP that deals with classifying text into some sentiment-related categories. The simplest approach is to have just two possible outcomes: positive or negative, but more detailed grading systems (e.g. from very bad to very good) can be implemented too. Moreover, emotion detection can be used to detect happiness, anger, frustration, or sadness, for instance.

There are many ways sentiment analysis or opinion mining can be applied to a wide range of different sectors. From automatically analyzing customer reviews of specific products, to monitoring general trends in brand reputation on social media, to keeping an eye on the competition, having a clear view of the opinions and sentiments of the public is key to having an impact on it.

Text Analysis, Extraction and Summarization

Several different specific applications may fall under this broader category, like key-word extraction, document classification, or optimized content-based document search. Even if these names sound strange, I am sure that at least the outputs of some of them are very familiar to us all.

Take COVID-19. With the public institutions ever more –rightfully– concerned about the spread of misinformation, the Big Tech had to follow along and set up automatic tools to label and remove content that may be potentially doing so. The technology behind this is NLP, automatically analyzing the content of such posts, and is similar to the one deciding whether the email you just received should be sent to the junk folder or not. Apart from these widely known use cases, such technologies can be of use in a business environment in order to automatically extract key information or summaries of documents, enabling optimized navigation and retrieval.

Speech Recognition

Speech recognition is fairly easy to understand. It refers to the process of transforming the sound waves we produce when speaking to written text that can be further processed, thus creating, for example, voice interfaces. As such, it is a key component of the virtual assistants we have reviewed before. In that case, speech recognition is the first step in a more complex NLP application, but it can also be of use by itself, for instance, to automatically transcribe calls.


Advanced uses of NLP technologies

All the aforementioned applications are of widespread use and quite common to us all, but NLP has many more exciting applications to offer.

Regulatory Compliance

Currently, companies have to devote considerable resources to elaborate different kind of policy documents in order to fulfill their regulatory obligations, from privacy, to information security, to environmental policies.

There have been interesting research outcomes regarding the automatic assessment of compliance with specific regulations, such as with EU’s GDPR, or others assessing sustainability reports’ readability. These technologies can help companies with the preparation of these documents, point at potential defaulting points, and enable a faster fact-checking whenever new regulation come into force.

Log Monitoring

One of the most promising applications of NLP goes hand in hand with the Digital Transformation process we are currently immersed in. Nowadays, almost everything around us has some piece of software running on it and, in most cases, the low-level way this software has to tell us about its current status is by generating logs.

Log messages are precisely generated to provide application developers and system operators with information that could help them, among other things, understand execution paths, find bugs or solve incidents. Generally speaking, when a problem occurs, logs are often relied upon for investigation.

Logs are usually a combination of plain language and some system-specific parameters that tell us more about what’s going on in the system. Wouldn’t it be great if we could use NLP to automatically read all these logs and warn us only when something has gone or is about to go wrong? Keep reading.

There is a handful of popular frameworks out there that collect log data and provide some anomaly detection feature. However, they work simply on numeric aggregations of the data and use a rule-based approach to match logs to existing templates. Clearly, the validity of this method is only supported by the handcrafted rule set, which is hard to customize, let alone keep updated. Moreover, the actual content of the logs is never taken into account.

By understanding the semantic content of the logs and considering in which context they appear, much more complex anomalies can be spotted. And to do all that in an automated way, relying on NLP to learn from the data, is what is defines the next level in log monitoring solutions.


Take control, optimize performance, enhance security

Since IT infrastructures are getting more complex and dynamic by day, it’s exponentially harder to ensure that they are efficiently monitored and secure. Advanced log monitoring solutions help businesses gain complete oversight of IT environments to detect vulnerabilities, troubleshoot issues, improve performance, and enhance security.

By automatically analyzing historical data, log monitoring solutions help businesses understand patterns and trends in activity within the infrastructure. Based on these insights they can, for example, see which processes waste resources and readjust resource management accordingly, which improves performance.

By monitoring infrastructure in real-time, these solutions detect issues when they occur, which enables businesses to instantly react to them, hence preventing minor incidents from growing into big problems. And by automating incident report and notification, we also eliminate the time and effort needed to detect an event and make it easier to respond to it, providing all the necessary contextual information summarized and highlighting the incident root cause.

XLAB’s AI team is committed to use AI, and specifically NLP, to help organizations solve their real-world problems by distilling the most advanced research outcomes and applying them in a simple, transparent way. We are fully devoted to the development of the most advanced log monitoring solution, powered by NLP, which is able to understand the semantics of logs, the normal behavior of the system, and alert operators when things start to go off. Trust us, all is in the logs!

Seizing control of your IT operations doesn’t just benefit your IT teams, but your business as a whole. Log monitoring boosts efficiency and productivity and guarantees higher uptime, better use of resources, and stronger security, while reducing costs. Are you ready to start leveraging AI for to boost your business success? Reach out to our experts and start reaping benefits of NLP-powered log monitoring solution.

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