Natural Language Processing advances digitalization and makes companies smarter

It's a valuable asset, yet many companies are unaware of its existence: language data. Companies can process language data and leverage their potential with Natural Language Processing.

Natural Language Processing advances digitalization and makes companies smarter
Lengoo Marketing Team
Mar 10, 2021

Businesses are aiming to become smarter, more digital, and more resilient. Natural Language Processing (NLP), a method from the field of artificial intelligence, can help pave the way to success. The required language data usually lie dormant in the content management systems, glossaries, or translation memories of companies – as a veritable hidden treasure. Those who manage to use them to their advantage, can automate company processes while boosting their efficiency, cutting costs, and gaining valuable insights.

NLP translates human speech for computers

Natural Language Processing is a subfield of AI. The technology builds on the foundation of Deep Learning, allowing machines to understand, interpret, and ultimately learn human language. Humans who are not skilled in programming, will be unable to untangle the mess of numbers and letters of coding language. In a similar vein, complex human speech is hopelessly imprecise, unstructured, and often ambivalent to computers. NLP uses methods from computer linguistics and computer science to bridge this gap between human and machine, transforming language into data with a numeric structure.

Hidden data provide useful insights

NLP can be used to record and extract vast data quantities – at speeds humans can never dream of achieving. After processing, companies can use the newly gained information to optimize their processes or derive specific recommended actions. To name just one example, companies can employ NLP to analyze language data from customer feedback or social media, and draw conclusions on customer satisfaction from them.

Language data and Machine Translation

We use the valuable language data of our customers for bespoke Neural Machine Translation. Experts in Data Science and Machine Learning leverage the potential of state-of-the-art tech to analyze, prepare, and enrich data. Deep Learning systems weave neural networks from the optimized datasets. These then analyze the language information, recognizing customer-specific characteristics, such as style and terminology, to ultimately create machine translations of near-human quality. The networks keep getting better thanks to automated feedback loops with human translators.

Digital processes with NLP applications

Investing some effort into language data stocktaking is a profitable venture for companies. Besides bespoke machine translation, there is a whole range of other possible applications for NLP.

1. Sentiment analysis

Sentiment analysis uses NLP technology to help computers understand the underlying meanings of human language. This allows machines to analyze moods and derive recommendations from them. Sentiment analysis furthermore allows companies to collect customer responses to campaigns in a targeted manner, or monitor social media comments for quick interventions when needed.

2. Chatbots

Language data can be used to train chatbots – certainly one of the most popular NLP applications. Specialists train the bots with language data, after which they can offer virtual assistance to Customer Support. They access massive datasets to respond to customer inquiries.

3. Automatic language detection

For automatic language detection, NLP is also capable of transforming human speech into a machine-readable format. Products of this nature are voice assistants such as “Alexa” and Apple's “Siri”. This NLP application also shoulders some burden of daily business: Once integrated into the company software, the technology transcribes calls or corrects texts, using company-specific parameters.

4. Keywords and buying stage

By analyzing language data, valuable insights can be gained in sales and marketing. In marketing, keyword analysis sheds light on the information customers are looking for. In sales, language data can be used to qualify intent. The analysis of language data from e-mails and chats helps identify at which buying stage customers are.