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5 Questions To Ask Before Getting Started With Data Annotation

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While it’s not always easy to turn raw data into smart data, there is one process that helps add vital bits of information to raw data – providing structure to data that is otherwise just noise to a supervised learning algorithm – data annotation.

Ultimately, artificial intelligence can’t succeed without access to the right data. Feeding it the right information with a learnable ‘signal’ consistently added at a massive scale is going to drive constant improvement over time. That’s the power of data annotation. However, before you begin with any data annotation project, it’s important to consider the following questions.


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5 Questions To Ask Before Getting Started With Data Annotation

  2. 2. Annotation plays a crucial role in ensuring your AI and machine learning projects are trained with the right information to learn from. It provides the initial setup for supplying a machine learning model with what it needs to understand and discriminate against various inputs to come up with accurate outputs. By frequently feeding tagged and annotated datasets through an algorithm, you’re able to establish a model that can begin getting smarter over time. The more annotated data you use to train the model, the smarter it becomes. DATA ANNOTATION
  3. 3. ANNOTATION IS THE SECRET TO HACKING AI • 80% of AI project time spent on data preparation* • Companies spend 5X as much on internal data labeling than with 3rd parties* • Annotation and labeling is essential for training AI and machine learning; it’s what makes them truly intelligent. • Even small errors could prove to be disastrous, therefore human-annotated data is essential • Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity *Cognilytica, 2019
  4. 4. ANNOTATION PROVIDES GROUND TRUTH FOR AI There are many different types of data annotation modalities, depending on what kind of form the data is in: SEQUENCING Text or time series from which there's a start (left boundary) an end (right boundary) and a label. CATEGORIZATION Binary classes, multiple classes, one label, multi-labels, flat or hierarchic, otologic SEGMENTATION Find paragraph splits, find an object in image, find transitions between speakers, between topics, etc. MAPPING Language-to-language, full text to summary, question to answer, raw data to normalized data
  6. 6. 1 | What do you need to annotate? • Text Documents • Images • Video • Web Documents • Audio Files Annotation can be applied to many types of assets:
  7. 7. 2 | Is your annotation accurately representative of a particular domain? Before you start labeling data, you should understand the domain vocabulary, format and category of the data you intend to use – also known as building an ontology. • Financial Services • Pharma • Healthcare • Legal • Regulation & Compliance Industries with unique rules and regulations for data:
  8. 8. 3 | How much data do you need for your AI/ML initiatives? The likely answer is as much data as possible, but in some instances certain benchmarks can be established based on the specific need (e.g. the past 10 years of SEC regulatory data).
  9. 9. 4 | Should you outsource or annotate in-house? Building the necessary annotation tools often require more work than some ML projects. But for many companies, security is an issue, so there is often hesitation to release data. But many companies have privacy and security procedures in place to address these concerns.
  10. 10. 5 | Do you need your annotators to be subject matter experts? Depending on the complexity of the data you are annotating, it is vital to have the right expert handle annotations. While several companies use the crowd for basic annotations, more complex data requires specialized skills to ensure accuracy.
  11. 11. Check Out 9 Data Annotation Best Practices from Leading Companies https://info.innodata.com/accelerate-ebook Nine best practices from industry leading data-driven companies ACCELERATE AI WITH ANNOTATED DATA