The mundane work of designing precise data annotation / labelling tasks

or, why did I add annotation tasks to Nocode functions if there is so much already on offer?

Annotating or labelling data is an essential step in the chain of data processing, for at least two use cases:

  • in supervised learning, labeled datasets provide a ground for the model to train on. Good annotations make good training sets, which impact greatly the quality of the model.
  • in models that are “rule based”, a dataset which is accurately labeled makes sure the rules and heuristics get triggered in the appropriate circumstances.

Annotating data consists for a person to point the absence or presence of a specific feature in a piece of text, an image, a video or audio content… For instance, the open source solution Label Studio offers tools to annotate audio segments, by adding tags in a very intuitive interface:

How are annotations managed, in practice?

Until ~ 2015, Amazon Mechanical Turk (MTurk) was the dominant solution to design tasks and crowd-source them to people recruited on the same platform. It has profoundly changed practices in the industry, and in academia as well. In 2016, 43% of behavioral studies featured in the academic Journal of Consumer Behavior were conducted on MTurk! (1)

This position evolved:

  • new actors emerged, integrating additional services
  • some actors positioned themselves on a specific segment: crowd-sourcing annotation tasks.

Today, querying “annotation platform” on your favorite search engine will return the names of Toloka, SuperAnnotate, Basic.ai, and many more.







Together, these companies and products coalesce into a new, specific industy proposing part or all of:

  • tools for annotating text, image, video, audio
  • project management (curation, quality control, time management, data security, etc.)
  • management of the workforce of annotators - including the setup of world-wide marketplaces
  • integration with the rest of the flow of data analysis
  • with industry-specific features (DiscoverText, for example, is well suited for academic needs and Twitter data)

Amazon has followed up these developments by adding an “Amazon SageMaker Data Labeling” (aka Amazon SageMaker Ground Truth Plus) to their MTurk offer.

Considering all this, why did I add an annotation tool to Nocode functions?

Isn’t it very redundant, given the plethora of offer? These are the reasons:

  1. To be effective, annotation tasks need to be precisely taylored to the case.
  2. A third party solution can be hard to fit in an existing chain of data processing

These two points boil down to “customization”. I need to run annotation tasks in ways that are not covered by the existing solutions. Specifically:

  • I need to run a Best-Worst Scaling task. This is suuuuper powerful and yet not on offer anywhere AFAIK. So I added BWS Tasks to Nocode functions, for my own use and for anyone else with the same need.
  • I need to categorize words, which is a super classic kind of task. But these words need to be highlighted in a sentence, to provide context to the annotator. Otherwise, how can the annotator judge the meaning of a word in isolation? That would be much harder and imprecise. So I need a task that allows a notion like: “annotate an object, which has a value (the word) and a ‘displayed’ value for the annotator (a sentence where the term appears, with some css to highlight it)”. For instance, to categorize the term “Oklahoma” I would need it to be inserted in a sentence for context, with some color effect to signal it to the annotator:

“To the red country and part of the gray country of Oklahoma, the last rains came gently, and they did not cut the scarred earth.”

I developed a specific function to do this highlighting, so that now tasks on Nocode function have the notion of item value / item displayed value with css formatting for the annotation of an item.

  • While the above seems trivial (adding some css…), it has cascading impacts on the UI. For instance, the solution now needs to allow for the import of two fields per item to annotate (the value of the item, and the displayed value with the css). This can be managed by defining a json schema for the data to be imported… but that would add another layer of complexity and pre-supposes the task designer is comfortable with a programmatic approach. I choose instead to have a click-and-point interface where two columns can be imported from a csv or Excel spreadsheet: one for the term, one for the sentence where the term is embedded. Very simple.

Yes, this is not industry-grade so I don’t expect it to be widely adopted. I just hope that it will improve the quality of the annotations for my research and that it will prove useful to small teams that need these functions specifically, without lock-in nor the complexity of adopting large scale solutions.

As always, your feedback is welcome!

Written on January 30, 2022