NLP: Use and meaning
NLP uses artificial intelligence (AI), among other things, to bridge the gap between man and machine, so to speak, creating a deeper understanding of data and content. The continuous, innovative development of NLP is driving automation, data analysis and improving the user experience in many industries.
The main functions of NLP can be summarized under the headings of understanding, generating and manipulating human language.
Typical use cases of NLP
NLP is used in various industries and situations. The most common examples include chatbots and virtual assistants, which enable companies to answer customer queries more quickly and thus improve the customer experience.
One of the most common use cases for natural language processing is the use of search engines such as Google or Bing: if the internet user clicks on a suggested result, the search engine interprets this as confirmation that it has presented the right result for the search query. In this way, the system is in a constant learning process.
NLP also plays an important role in document processing, as it increases efficiency and accuracy. The integration of NLP into document processing enables the automation of complex tasks, the analysis of large volumes of text and the optimized use of information. This helps to optimize business processes, gain insights from data and increase productivity. Specifically in the area of invoice data capture, NLP can be used to truly understand invoice content and line items based on context, structure and intent, i.e. at a human level.
NLP can also be used to automate email routing: The content of emails is analyzed and thus routed to the appropriate department or person. This application of NLP not only helps to increase efficiency, but also improves the customer experience by reducing waiting times.
endless possibilities.
ExB is an Intelligent Document Processing platform that transforms unstructured data from any type of document into structured results. Our AI-based software can not only extract all relevant information from your documents, but also understand them. This allows you to automate your processes and save both time & money, while improving your customer experience and employee satisfaction. Win-win.
How Natural Language Processing works
NLP is highly complex and uses various algorithms, techniques and other areas of artificial intelligence, such as machine learning. While machine learning and NLP are closely related, NLP – as the name suggests – focuses on natural language processing, while machine learning covers a broader range of applications and techniques. The process of algorithmic natural language processing can be broken down into the following steps:
- Text or data preparation: this involves collecting data and preparing it for processing. This can include the removal of stop words, stemming (reduction to word stem) and lemmatization (transformation to basic form). Words are also converted into numerical vectors in order to capture the meaning (semantics) of words based on their context.
- Entity and structure analysis: In this step, relevant entities such as names of people, places, organizations or dates in the text are identified and categorized. The syntactic structure of the text is also analyzed.
- Semantic analysis and classification: The meaning of the text is recorded and the text is classified into predefined categories.
- Information extraction and generation: Specific information is extracted from the text and, if required, text is generated to produce human-like or automated responses.
Depending on the use case, additional steps or specialized techniques may be required.
Academic NLP differs to some extent from practical NLP, as the focus and objectives may be different: Academic NLP often focuses on exploring fundamental concepts, algorithmic innovations and theoretical models to push the boundaries of natural language understanding. The research questions are usually more complex but less application-oriented. Practical NLP, on the other hand, focuses on developing efficient, scalable solutions and methods that support real-world applications such as chatbots, text analytics or document processing.
High-end parsing
High-end parsing is a method or specific technique within NLP that focuses on the advanced syntactic analysis of texts. High-end parsing goes beyond basic syntactic analysis by using more complex models and techniques to capture the structural relationships between words in a sentence more accurately and contextually. This allows deeper insights into the meaning and context of texts to be gained.
What challenges does NLP face?
Algorithmic language processing faces various challenges that need to be overcome in order to further improve the efficiency and accuracy of natural language processing. Some of these challenges are: Polysemy (ambiguous words), context understanding, multilingualism, irony, but also data protection and ethics.
Natural language processing is an exciting and promising area of artificial intelligence. With the ability to understand, generate and manipulate human language, NLP offers many applications and benefits. Despite some challenges and limitations, NLP has the potential to improve and simplify many aspects of our lives and work.
How ExB uses Natural Language Processing
ExB uses a unique, multimodal approach that combines computer vision and the latest deep learning algorithms with natural language processing to fully understand any document in any format. This combination not only enables the precise analysis of documents and text content, but also enhances the ability to interpret visual elements such as images, diagrams and graphics and incorporate them into the overall context.
