Annelies Hall

Key Skills

Software

Mercor

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Computer Code Programming

Document

Geospatial Tiled Imagery

Image

Video

Top Label Types

Object Detection

Point Key Point

Prompt Response Writing SFT

Question Answering

Relationship

Text Generation

Text Summarization

Tracking

Transcription

Translation Localization

Freelancer Overview

I am an experienced linguist and translator with over 7 years of hands-on expertise in data annotation, transcription, and semantic analysis for multimedia content. My academic background, including a Ph.D. in Linguistics from Stanford, has equipped me with advanced skills in emotion and tone detection, discourse analysis, and the development of annotation frameworks. I have managed complex projects in remote environments, ensuring linguistic accuracy, cultural sensitivity, and clarity across diverse domains such as legal, medical, and corporate communications. I am highly proficient with industry-standard tools such as SDL Trados, MemoQ, ELAN, and Praat, and have a proven track record of enhancing data quality for AI training, establishing annotation standards, and delivering precise, context-rich labeled datasets for natural language processing and machine learning applications.

Labeling Experience

Data Labelling experience

MercorVideoPoint Key Point

I worked on a large-scale data labelling project to prepare training datasets for AI models in both text and image domains. The scope included designing annotation guidelines, performing labelling, and ensuring data quality for model development. Specific Labelling Tasks - Text: sentiment classification, intent detection, and entity tagging (NER). - Images: bounding box annotation, object categorization, and attribute tagging. - Structured data: verifying and standardizing categorical labels. Project Size - ~50,000+ records labelled across text and image datasets. - Team of 5–7 annotators over a 3‑month period. Quality Measures - Clear annotation guidelines for consistency. - Inter‑annotator agreement checks. - Weekly audits and spot checks. - Accuracy threshold maintained above 95% before dataset acceptance.

I worked on a large-scale data labelling project to prepare training datasets for AI models in both text and image domains. The scope included designing annotation guidelines, performing labelling, and ensuring data quality for model development. Specific Labelling Tasks - Text: sentiment classification, intent detection, and entity tagging (NER). - Images: bounding box annotation, object categorization, and attribute tagging. - Structured data: verifying and standardizing categorical labels. Project Size - ~50,000+ records labelled across text and image datasets. - Team of 5–7 annotators over a 3‑month period. Quality Measures - Clear annotation guidelines for consistency. - Inter‑annotator agreement checks. - Weekly audits and spot checks. - Accuracy threshold maintained above 95% before dataset acceptance.

2025 - 2025

Education

Work History