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Appen
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Data Annotation Tech
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TextAs an expert in ontology and taxonomy, I am well-equipped to structure and organize data, which is crucial for AI and machine learning tasks. Ontologies define relationships between entities, while taxonomies categorize data into hierarchical frameworks. This structured approach ensures that data is consistently labeled and relationships are well-defined, making it easier to convert data into numerical vectors for machine learning models. My 10 years of data modeling and classification experience, combined with my additional hands-on data annotation experience, helps create a solid foundation for data representation and analysis. My skills in ontology and taxonomy development, along with my data annotation experience, make me ideal for data labeling and AI training. Accurate and consistent labeling is essential for training AI models to recognize patterns and make precise predictions. By leveraging my knowledge, I can ensure that data is labeled correctly, reducing bias and enhancing model accuracy. My ability to define clear relationships and categories within the data ensures that AI models can learn effectively from labeled examples, leading to more accurate and efficient outcomes.
Labeling and prompt/response evaluation of images for object recognition, bias, and safety.
Nine months of experience at Data Annotation Tech, primarily evaluating text responses to user prompts regarding text- and image-based submissions. Evaluation criteria include truthfulness, instruction following, tone, conciseness, relevance, organization, and safety. Additional projects include prompt engineering to test spontaneous response, rating of coworkers' evaluations,
Four courses toward Master of Science degree, Computer Information Systems
Bachelor of Arts, Journalism
Program Associate - Data Analysis
Ontology and Taxonomy Developer
