NLP technology revolutionizes document processing
The digital age has not only begun, we are in the middle of it. Companies need to process enormous volumes of documents digitally, and it’s not getting less, it’s getting more - from emails with attachments to invoices, contracts and purchase orders to scientific publications.
Processing by people can take a lot of time and be very costly as well as error-prone due to inefficiencies. Natural Language Processing (NLP) can be used to automate many document-related processes. This saves time, resources and thus costs. At the same time, it increases customer satisfaction and is an important lever for companies to compete or secure market advantages.
We explain the background to AI technology and provide insights into its potential applications for automating document processing in companies.
Recognition of information independent of document structure and wording
Evaluation of long, complex documents possible
Fast editing or digitization of large amounts of text
Automation of processes that were previously only feasible by (trained) personnel
Automation of even complex processes
Reading out a standardized invoice format has been possible for a long time. Until now, it was difficult or even impossible to process unstructured texts that were not available in a standardized format. Using Natural Language Processing, information can be automatically identified and extracted in a structured way, even in complex texts.
Document processing using Natural Language Processing offers numerous advantages for companies - from efficiency gains and error reduction to improved data quality and customer service. By using the technology, companies can optimize their processes on the document and thus increase their competitiveness and business success.
Natural Language Processing is a subfield of artificial intelligence that deals with the interaction between human language and computers. It involves programming computers to understand, analyze and generate human language. Natural Language Processing is already used in many applications such as chatbots, for speech recognition, translation, information extraction and document analysis. In particular, automated information extraction and document processing are great levers in the digitization and automation of business processes.
Documents of all kinds - from emails with attachments to contracts and clinical documentation - can be analyzed and the information they contain processed as structured data.
The NLP models
In order to be able to extract specific information in a structured manner, Natural Language Processing uses language models that have been trained on the basis of sample documents. Deep learning techniques such as neural networks are used for this purpose. The models relate elements of a text - the text modules - to each other or analyze them in context. The relationships between the data/elements serves as a basis to understand their meaning in context. The model thus “learns” to recognize certain information reliably.
The NLP models are integrated into the overall process or the document processing software and ensure that the specific information relevant to the process is read out.
One of the biggest challenges in Natural Language Processing is the diversity and complexity of human language. There are dialects, slang expressions, idioms, typos, and a wide variety of formulations for the same message. Natural Language Processing systems use techniques such as morphology, syntax, and semantics to still “understand” text. Morphology refers to the analysis of words and their structures, while syntax refers to the relationships between words. Semantics deals with the meaning of words and sentences.
Polysemy means that a word has several meanings. An example is the word “bank”, which can mean both a bench and a financial institution. But ambiguity can also be caused by homonyms and homophones, such as the word “bank” (financial institution) and “benches” (seating). The latest Natural Language Processing technology nevertheless recognizes the content - through contextualization.
Without appropriate training, Natural Language Processing models cannot reliably recognize information. An NLP model is trained with sample data. It learns by analyzing the structure, grammar, and meaning of words and phrases in this data. The quality of the training data is critical to performance.
Efficiency in business
Natural Language Processing on the document can be used in the most diverse areas of companies and in any industry for process automation. The following are some examples of the use of NLP technology for document processing - without claiming to be exhaustive.
Order and inquiry management
Thanks to state-of-the-art Natural Language Processing software, e-mails including attachments (e.g. order documents) can be read at a human level - quickly and accurately. With recognition rates of up to 99% even for multiple items and unstructured documents. This enables the automation of work steps that were previously only possible manually. Direct comparison with existing data, such as product or service catalogs, can be performed. This means that a quotation can be created in record time and the workload of the internal sales team is greatly reduced. Even the fully automated creation of orders in the ERP system based on the extracted and already matched data is possible.
Contract management and analysis
Using Natural Language Processing, even complex contracts can be checked for specific content in a structured manner. This speeds up contract management processes enormously. The AI automatically recognizes important data such as deadlines and contracting parties. In addition, Natural Language Processing can even be used to identify potential risks - the AI reliably recognizes relevant or potentially critical clauses and can even make an initial assessment. Companies therefore not only save time in administration, but can also gain more security. Even large volumes of contracts can be checked for specific features.
Email and input management
Fast and targeted processing of incoming e-mails not only increases customer satisfaction, but also improves overall efficiency in the company. Natural Language Processing can be used specifically in input management to recognize and extract information from incoming messages including their attachments. E-mails can be “read” and specifically assigned to the appropriate subsequent process step or even processed completely automatically.
Automated invoice capture enables companies to save time, money and resources while significantly reducing errors. By using Natural Language Processing, data on invoices is recognized more reliably than by solutions based solely on text and position recognition. AI recognizes information in context, regardless of format, structure, and wording.