Data labeling might feel like a walk in the park at the initial stages but you may go wrong when you start annotating. Anticipating what can go wrong and why will help you quickly detect and fix errors. Now let’s dive deeper to know the four common errors that annotators make when labeling data.
White spaces and punctuation:
One of the biggest blunders annotators makes when labeling data is inconsistent labeling of trailing and leading white spaces and punctuation. This has the highest chance of lowering agreement scores and creating unwanted ambiguity or confusion. For example, one may label “data“, while the other labels it as ”data” or “data”. This kind of error may frustrate you because humans really don’t care or bother about it and secondly it is technically correct to do so. But to your surprise, these errors will be the root cause for other such errors. One best way to avoid this error is to use a labeling tool that explicitly indicates with visuals to annotators the trailing and leading white spaces and the punctuation.
Nested annotations:
Another biggest annotation error that many annotators commit is nesting annotations. Annotations that are annotated like a tree or brat will look clear and is easy to comprehend. On the other hand, a nested annotation will create much confusion and may disrupt the structure. From a UX angle, these methods like a brat or tree annotation work but they require downstream models that can manage complex and non-linear structures in the input and the output as well. It is always wise to start annotating at the finest resolution possible and then start employing post-processing to capture the fundamental structure.
Adding new entity types amid a project:
While working on a project you might be in a need to add new tags that you missed out or thought wouldn’t be required for processing. Later, when you need new tags, creating and adding them and continuing to work wouldn’t make sense. By doing so, you will note that the new tags will be missing from the documents that were annotated before adding the new tags. This simply means that the test set will be wrong and the training data won’t contain the newly listed tags. You could start over but it is waste of time and resources. So what is the best solution? Start over! Yes, you can start right from the beginning but make sure that all the tags are properly captured. One easy way to do this is to use pre-annotations to the tags that are currently in use and then add new tags, by default method, which will not impact the process.
Overwhelming lists of tags:
Want to increase overhead costs and produce poor-quality data? Well, the solution is simple. Ask your annotators to work with overwhelming lists of tags. Yes, adding too many tags will leave the annotator with several choices, which ultimately slows down the process and lets them generate low-quality data. To avoid this type of error, we suggest you club your tags under a broad spectrum to save a lot of your time, cost, and most importantly effort.
Conclusion:
Data labeling has to be done quickly and at the same time with the highest accuracy without any errors. Create a quality annotation pipeline by foreseeing problems that are very common and properly accommodating them. Leave all your data annotation worries to us and experience a hassle-free, smooth and seamless workflow.
