Ensure that your machine learning models have the right training data to support your goals
Solutions that rely on machine learning require high volumes of data samples to train them to think and act like humans. But not just any data—high-quality, human-annotated data is needed to create the right customer experience. Appen has the resources to help you quickly scale your data annotation needs for a variety of data types – including text, image and video – in over 180 languages and dialects.
Appen creates high-quality, human-annotated datasets to train machine learning algorithms to mimic human thought. Annotated data enables richer, more valuable, and more directly usable applications.
Examples of our capabilities include:
- Tagging gestures and facial expressions in video
- Part-of-speech and other morphological tagging of corpora and lexicons
- Tagging of words that are profane, sensitive, neologistic or misspelled
- Syntactic and dependency tree-banking, including the identification of co-reference
- Semantic annotation of text, including named-entity identification for search, sentiment analysis, and data-mining applications
- Transcription and time-stamping of speech data, including transcription of pronunciation and intonation
- Identification of language, dialect, and speaker demographics
- Tagging of glass breakage, gunshots, and aggressive speech for security and emergency hotline applications
- Image annotation to accurately describe image content for use in training computer vision systems
With access to a curated crowd of over 1 million worldwide in over 130 countries covering more than 180 languages and dialects, Appen is uniquely positioned to provide you with high-quality, human-annotated datasets for your target markets.
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An Introduction to Machine Learning Training Data [White Paper]
When it comes to your AI strategy, have you considered the amount and type of data you’ll need to effectively train your machine learning models? This white paper aims to help business executives embarking on—or looking to improve—their machine learning initiatives, and covers why machine learning requires a high volume of data, the importance of high-quality data, and what data sources to consider.