Named Entity Recognition (NER) plays a vital role in Natural Language Processing (NLP) tasks, extracting valuable information from textual data. This study addresses a gap in NER research by comparing the effectiveness of cloud-based NER tools (Azure NER and Google Cloud NER) and a popular open-source tool (SpaCy) for recognizing named entities in both English and Polish text. Text data is imported into a PostgreSQL database and processed by each NER tool. The extracted entities and their labels are stored in a dedicated SQL Entity table, enabling performance evaluation across different languages and entity types. This research contributes to the field of NLP by investigating the suitability of cloud-based NER tools for multilingual tasks, particularly those involving Polish text, which presents unique linguistic challenges. By analyzing the performance of these NER approaches, the study provides valuable insights for selecting the most effective NER technique for specific NLP applications, especially when dealing with multilingual content.
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