Dia Trambitas is a computer scientist with a rich background in Natural Language Processing. She has a Ph.D. in Semantic Web from the University of Grenoble, France, where she worked on ways of describing spatial and temporal data using OWL ontologies and reasoning based on semantic annotations. She then changed her interest to text processing and data extraction from unstructured documents, a subject she has been working on for the last 10 years. She has a rich experience working with different annotation tools and leading document classification and NER extraction projects in verticals such as Finance, Investment, Banking, and Healthcare.
Navigating the world of medical research often feels like diving into an ocean of information without a map. For researchers, healthcare professionals, and clinicians, effectively conducting literature reviews is critical...
Side-by-side viewing and comparing different annotations made by various annotators on text documents present several challenges. Firstly, aligning the annotations for accurate comparison can be difficult, especially if the annotators...
Overall, de-identification in today’s data-driven world is a critical practice that helps balance the benefits of AI and big data with the need for privacy and compliance, facilitating both technological...
In industries like healthcare, in which regulatory-grade accuracy is a requirement, human validation of model results is often a critical requirement. While models handle the legwork, the Generative AI Lab...
Classifying PDF documents using text-based classification models is a powerful capability Generative AI Lab provides. Users can now pre-annotate and classify images and PDF documents with over 1500 pre-trained models...
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