Damian Romero Profile picture
May 2 β€’ 8 tweets β€’ 3 min read
Annotation guidelines for ML 101 (basic concepts) 🧡

✨ The results of supervised learning approaches are only as good as the annotations they are based on.

✨ Annotations are only as good as the guidelines that annotators rely upon to direct their efforts.

⬇️
What is data annotation? πŸͺ„

✨It is the task of associating entries in your data with additional information

✨It is also known as "coding" or "labeling"

✨It is crucial for both qualitative and quantitative analyses

⬇️
Supervised learning basics πŸ€–

✨ In AI, data annotation is the bases for the supervised learning approach

✨ Supervised learning uses annotated datasets or collections of data points with associated labels

✨ The model learns to predict labels from these annotated datasets

⬇️
πŸ€– More about supervised learning πŸ€–

✨ Although other modeling techniques exist, supervised learning is very much used in academia and industry

πŸ“™ This blog post from @semihyagcioglu in @Springboard talks about what supervised learning is:

springboard.com/blog/data-scie…

⬇️
So what are annotation guidelines? πŸ“

✨ Annotation guidelines are conventions that annotators use during their work

✨ They are frequently designed by project managers or expert annotators

✨ Ideally, the people who write them are a group of experts
What are the essential components of language annotation guidelines? πŸ“

πŸ“– Task description and objective

πŸ“– Annotation scheme and examples

πŸ“– Task instructions

πŸ“– Glossary of terms (encouraged)
πŸ“… Later this week I will...

✏️ Talk about the creation of annotation guidelines and good practices

✏️ Offer a quick walk-through of a methodology for their refinement
🏁 🏁 🏁

Last thoughts πŸ’­

#NLProc #DataScience tweeter I appreciate your views

πŸ’« For an example of supervised learning using πŸ’™ @spaCy πŸ’™ and Jupyter notebooks, you may find this YouTube tutorial from @OxyKodit which I find very useful πŸ’«

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