Text Analysis

Named Entity Recognition

Natural Language Processing (NLP) and Artificial Intelligence have revolutionized text analysis. NLP refers to the ability of machines to understand human speech and produce natural-sounding responses. Named Entity Recognition (NER) is a sub-discipline of NLP that aims to identify and classify named entities in texts. NER uses machine learning algorithms to recognize patterns in texts and partition entities into a given set of classes. NER is used in various applications such as information extraction, automatic translation, and chatbots, and can help efficiently analyze large amounts of text and extract important information. In the construction industry, for example, NER can be used in the processing of project documentation. Here, the automatic identification of entities such as project names, employees or important deadlines can help speed up the information processing and minimize errors.

Table Detection

Information extraction from tables is an important area of AI research. This area deals with extracting structured data from tables and storing it in a machine-readable format for further processing. To extract information from tables, algorithms such as table analysis, cell classification, column recognition, and row identification are used. Machine learning is used to detect patterns in the data and improve the extraction of information.