The configurations for a project-specific labeling scheme is defined via. It has a rich set of features such as integration with external resources including Wikipedia, support for automatic text annotation tools, and an integrated annotation comparison. Top Text Annotation Tools brat (Browser-Based Rapid Annotation Tool)īrat is a free, browser-based online annotation tool for collaborative text annotation. Or maybe it should be removed completely. This might be a sign that the label needs more clarification about its meaning, or that it needs to be slit into separate labels. But there seems to be a lot of confusion about where the label food_order should be applied. You can see that they both agree on all the things labeled order_time, and they mostly agree on the food_item. In the example above, annotator 1's labels are in the columns and annotator 2's labels are in the rows. You could use a measure of inter-rater reliability like Cohen's kappa, Scott's Pi, or Fleiss's kappa for this. This enables annotators that are unsure about an annotation to flag it, allowing it to be double-checked later.Īnother helpful method is to have some annotators look at the same data, and compare their annotations. One handy technique is to use a flag to denote confusion or uncertainty about an annotation. ![]() Well, how does one even do that? You could go through all of the text again, but that’s inefficient. One often overlooked thing is checking the quality of your annotations. So if a new label is added, or if the meaning of a label changes, everyone has easy access to the updates.Ĭhecking The Quality Of Your Text Annotations ![]() I recommend that you define your labels in a central shared location and keep this information up to date. Usually, there is a team of people that need to agree on what the labels mean. But labels like intent_1, intent_1_ver2, and unnecessary acronyms make it harder to quickly apply and check labels.īesides that, it’s unlikely that one person is going to be annotating everything on their own. ![]() food_item and time_of_delivery are good, straightforward labels that describe what you’re annotating. The first thing you can do to make the life of your annotators and developers simple is to keep the labels simple and descriptive. Let’s say you have this piece of text in your corpus: “I am going to order some brownies for tomorrow” This additional information can be used to train machine learning models and to evaluate how well they perform. Text annotation is simply reading natural language data and adding some additional information about it, in a machine-readable format. But if you want to know how well it's doing in production, you'll have to annotate text at some point. And you didn't need to do all the text annotation for training yourself. Maybe you were lucky enough to have a large pre-annotated text corpus. So if you're doing any type of supervised learning in your natural language processing pipeline, and you most likely are, data annotation has played a role in your work. In order for humans to rely on machines, machines need humans first to teach them. ![]() Even with all the recent advances in machine learning and artificial intelligence, we can’t escape the irony of the information age.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |