Natural Language Processing (NLP)

Use Natural Language Processing (NLP) to reap added value from your textual data and access important information faster!

Only the very smallest amounts of company data are available in the form of easy-to-read, processed tables. Instead, in the majority of cases, information is buried within texts – some of which are in other languages – preventing it from being quickly captured. We view Natural Language Processing as a methodological toolbox that enables us to develop intelligent, automated solutions to text- and language-based problems. NLP can tackle everything from seemingly simple tasks, such as predicting missing words in cloze texts, to complex challenges like machine translation and automatic text summarisation.

Wondering if NLP could provide a solution to your company’s problems? Take a look at the following scenarios:

Information Extraction

The storage and processing of structured data is part and parcel of day-to-day business for many of today’s companies. Automated information extraction enables you to integrate information from relevant documents into your database or enterprise search solution. This gives you direct access to essential details of your contracts, orders, invoices, and specifications. In addition, it also simplifies document monitoring using anomaly detection, for example.

Automatic Text Summarisation

In the field of automatic summarisation, Natural Language Processing helps to reduce large documents to their key messages. If, therefore, you are faced with the challenge of integrating different views and statements from different sources, NLP enables you to extract this content automatically, allowing you to make decisions based on concise, comprehensible information. This is useful in a wide variety of application areas. The ability to summarise current news reports can, for example, be of considerable relevance when making decisions related to high-profile events. For corporate managers, on the other hand, detailed reports and explanations can be compressed into short, management-friendly texts without the need for human intervention.

Automatic summarisation using deep neural networks is a hot research topic at inovex. We have experience in summarising German and English texts to create both academic and customer data. In our blog, for example, we describe the challenge of Summarising long texts using sequence-to-sequence models.

Text and Document Classification

You can use classification models to organise and structure your text data. These models learn to divide documents into predefined categories based on their content and structure.

Does an email contain important information, or is it spam? Is a document a contract or an invoice? Assign intranet and blog posts to topics. Evaluate user reviews on popular rating platforms. Use sentiment analysis to discover how people feel about your products or brands by analysing mentions in social networks. NLP also enables you to use intent detection to turn feedback directly into actions, such as “reply to customer” or “refund cost”.

Exploration & Visualisation

While illustrative visualisations based on structured data are more or less self-evident, they are not quite so intuitive when the data is unstructured, as it is in the form of text and documents. In such cases, you can use knowledge maps to visually explore your document collection. NLP can be used on your data to combine different aspects of your documents (existing classes, frequently occurring words and phrases, similarity of documents) to support you in your decision-making processes. We have implemented this type of visual exploration on a collection of patents.

Cognitive Search

We consider cognitive search to be the next evolutionary step in search technology. This new generation of intelligent search applications draws its strength from successfully combining traditional search technologies with natural language processing and recommender systems. While the use of recommender systems takes the user-specific component of search applications to a new level, NLP helps to improve the actual search process. NLP increases comprehension of the content contained in unstructured data and facilitates improvements to search queries themselves (query expansion, NER, topic identification).

Take a look at our experiences implementing cognitive search as part of our collaboration with, a German online marketplace for buying and selling vehicles.

How We Can Help You

Were you able to identify your company-specific NLP problem in one of the scenarios described? If so, please contact us! We provide a wide range of artificial intelligence services, from implementing proofs of concept to developing productive systems.

If you are still undecided or need further inspiration, we will be happy to assist you with advice and training.



Would you like a consultation on this subject?

Call us or send us an E-Mail. We look forward to advising you.

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Stefan Igel

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Stefan Igel

Leadership Team Data Management & Analytics