![]() To save time and resources, preprocess your dataset to exclude anything that doesn’t need to be annotated, like duplicated or noisy data (such as URLs or garbled text). Preprocessing: How do you need to clean and filter your data? If that doesn’t give you enough interesting or relevant data points, you would want to be more intentional about sampling (e.g., use even sampling). You would use representative sampling if you want to train or evaluate your model on the natural distribution of data. You’ll also want to decide how much noise to tolerate in your dataset. However, you’ll want to consider whether to do representative sampling or sample evenly across all domains. Your data should reflect the domains where your model will be applied-if you’ll be creating a natural language model to assist users on web and mobile, you should sample communications from both areas. To prevent overfitting, you’ll want to think through how to sample across different times, locations, and contexts. Sampling has a huge impact on your annotation task and model. Sampling: Where should you get your data from? A thorough, hands-on approach will help you stay one step ahead of potential issues and nuances that could come up during annotation. There are no right answers here-you’ll be guided by your machine learning use case and requirements. Getting ready for annotationīefore annotation can begin, the data science team will need to prepare the data and design the annotation task. ![]() If you invest in annotation and think through each step, you’ll almost certainly get better data as a result-so you can save time and budget in the long run, and build better models. We’ll cover choosing the right data, working with annotators, running quality control, and more. In this article, we’ll offer best practices for teams that use annotation to create high-quality datasets, focusing particularly on complex, subjective tasks using textual data. Regardless of the annotation task, there are certain repeatable strategies and processes that are useful if you’re a data science team member, computational linguist, ML practitioner, or researcher. For example, to create a gold dataset that will be used to build a model capable of correcting grammatical mistakes, annotators might be asked to identify the grammatical mistakes for a wide range of sample sentences. In annotation, humans label or transform data inputs into so-called “gold data” that informs what machine learning practitioners are trying to model. And to build a high-quality labeled dataset, it’s essential to have a great annotation process. The idea is that your model is only as good as the data you use for training and evaluation. When working with language data to develop machine learning (ML) models, there is a movement toward prioritizing data quality over data quantity. This article was co-written by Analytical Linguists Lena Nahorna and Lily Ng.
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