| The quantity of data and documents created on a huge scale has recently increased. This signifies a rise in unstructured textual information. Extracting sensitive data is usually done manually, following certain rules, which means additional time costs and elevated chances of errors and non-performance. There is a growing need to automatically solve these types of problems, leading to the need of utilizing methodologies that are more intelligent than those previously used. The likelihood of automating this kind of assignment might significantly facilitate compliance with safety policies and imposed regulations. This work aims to highlight the status of Named Entity Recognition (NER) by evaluating models and presenting their overall presentation. It also describes the conflicts and factors that affect the perception of a given entity. |
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