AI-based document classification and text classification explained simply
AI-based document classification and text classification explained simply
AI-based document classification plays a crucial role in the digital age. But why is that? In today’s digital world, data is a valuable commodity. But as data volumes grow, the flood of information quickly becomes overwhelming. This is where AI-based text classification comes into play – a powerful tool that brings order to chaos.
Imagine a huge library, filled not with books, but with billions of websites, articles and data. Without an efficient system, finding the information you need would be a Herculean task. AI-based text classification helps us to overcome this challenge.
What is AI-based document classification?
Text classification makes it possible to automatically divide texts into different categories, which makes data processing much easier. At MORESOPHY, we have developed a special model called “Content Categories”, which covers over 700 different subject areas in a four-level hierarchical structure. This enables a precise and detailed classification of content – a decisive advantage for content audits and market analyses.
Read this article to find out how the classification of risks in documents works.
How AI models work to classify documents
Now it’s getting a bit technical. If you want to understand the basics of how computers can understand text, read here: In this article, we have clearly explained how computers can understand text.
AI models for content classification usually work with machine learning (ML) algorithms, in particular supervised learning. In the training phase, large amounts of already classified data are presented to the model for “learning”. This data is used to enable the model to recognize patterns and correlations. The classification of text content by AI begins with the pre-processing of the text. As the “raw” text cannot be processed by machines, it is converted into a numerical format. This is done using techniques such as the creation of n-grams or word embeddings such as Word2Vec or BERT. The resulting vectors can represent information on word occurrence, semantic relationships between words or even contextual information in a machine-readable format.
After pre-processing, the transformed text is passed through the neural network. These networks are able to analyze high-dimensional data (text vectors) and recognize patterns.
The actual classification process then takes place in the output layer of the neural network. Here, the processed text vector is converted into probabilities (confidences) for each possible category. The category with the highest probability is then selected as the prediction of the model.
What is the AI-based document classification model “Content Categories” from MORESOPHY?
Our specially developed AI model for the thematic categorization of texts covers over 700 different subject areas, which are organized in a four-level hierarchical structure. These categories cover all conceivable subject areas, so that all content can be classified very finely into different categories with different levels of granularity. This enables precise and detailed classification, ranging from rough topic recognition to specific subcategories. As in (almost) all areas of artificial intelligence development, the preparation of the data requires around 80% of the effort. This is why MORESOPHY, for example, employs many computational linguists who continuously revise the classes and keep the category systems up to date.
If we give our model the task of reading a document on the taxation of pensions, it will tell us after a few milliseconds that the document is a topic from the area of personal finance, more precisely a topic from the area of retirement planning and the taxation of private individuals.
When applied to large volumes of content, a good thematic overview can be quickly obtained, e.g. for content audits and market analyses. If the analysis is deepened, the relevant content can also be identified and analyzed precisely at the level of individual topics.
In addition to the topics identified, the AI model also outputs the confidence level. The confidence level gives you an indication of how “reliably” the artificial intelligence has made the assignment to a certain category.
The model analyzes your content and assigns it to a taxonomy of content categories. The model has been trained with billions of pieces of data and can be applied to both web and corporate content.
The 4 most important advantages of AI text classification
The advantages of content classification using AI models are manifold. The most important ones are
- Increased efficiency: By automating the classification process, companies can save an enormous amount of time and resources that would otherwise have to be spent on manual sorting and tagging.
- Improved data analysis: AI models for classification enable a deeper and more precise analysis of content. Thanks to the uniform classification of large volumes of data (big data), whether internal company data or data from the open web, comprehensive market or company analyses can be carried out quickly, which would not be possible without the support of artificial intelligence.
- Personalization of the user experience: By precisely classifying content, companies can create personalized content for their target groups and display personalized content for different target groups (e.g. in advertising). Among other things, this improves customer loyalty and significantly increases conversion rates. You can see how this works in the CONTEXTCLOUD .
- Automation and scalability: No matter how large or complex the amount of data is, AI models such as our Content Categories Classification model can process and classify it in a matter of seconds. This enables easy scaling and adaptation to rapidly changing business requirements. In addition, these models can be flexibly integrated into business operations as required. MORESOPHYoffers its AI models as an API service, in data pipelines or in the CONTEXTSUITE AI hub, for example.
AI-based text classification is more than just a data organization tool, it’s a strategic advantage that helps companies stay competitive in today’s data-driven world.
Challenges in AI-based content classification
As impressive as the benefits of AI-supported content classification are, the use of AI is always associated with challenges and risks.
The biggest challenge for a good AI model is data quality. Clean and correctly structured data is essential for an AI model that classifies accurately. However, such data is not always available and its preparation can be time-consuming. To overcome this challenge, data pipelines can be connected upstream of the AI model to continuously monitor the data quality and improve it with the help of other AI models if necessary.
The world of content is a dynamic world, and AI models need to be flexible enough to cope with change. That’s why our computer linguists work tirelessly with AI engineers to adapt the models to the constant changes.
Another challenge: according to a Deloitte study, 68% of all German entrepreneurs surveyed see risk management as the biggest problem when using AI. The reason: AI models are often perceived as a “black box”, as it is generally not possible to check the reliability and accuracy of either the training data or the results. But this does not have to be the case. MORESOPHY, for example, recognized this early on and works consistently according to the principles of Trusted AI and Transparent AI. This means that all of MORESOPHY‘s AI solutions can be checked for reliability at any time. MORESOPHY recently received another grant from the Federal Ministry for Economic Affairs and Climate Protection for this.
And finally, there are ethical considerations. How do we ensure that our AI decisions are transparent and comprehensible? At MORESOPHY, we are aware of this responsibility and are continuously working on solutions that are not only technically forward-looking, but also ethically justifiable. MORESOPHYhas also repeatedly received funding for this.
Conclusion
AI-powered content classification is an essential part of today’s digital landscape and is just one of many AI models being used to analyze data and improve data quality.
It helps us to manage the immense flood of information and organize content in a targeted and meaningful way. While the technology offers impressive benefits, such as the efficient categorization of content and the creation of personalized user experiences, it also brings challenges, particularly in terms of data quality and ethical considerations. Despite these challenges, AI-powered classifications will become increasingly important. In the course of the digitalization and automation of tasks, we see a strongly growing demand for customer-specific classification models.
Do you have any questions, or do you need your own special AI classification model? Then please write to us at: info@moresophy.com
Project manager
Andreas studied Technology & Media Communication and is primarily responsible for internal and external communication and documentation within the company. This gives him an optimal overview of the various technologies, applications and customers of MORESOPHY.
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