# Semantic Annotation in text with curlie.org

I came across curlie.org (previously known as the dmoz taxonomy) and I'm interested to see how I could best start tagging a given text, with concepts from that taxonomy:

• Are there any tools out there that do semantic annotation based on a taxonomy (I couldn't find any)
• How would one go about making such a semantic annotation process

I know this question might be a large to answer in a short reply, but any pointers are greatly appreciated.

• Could you add some extra info about the kind of annotations you would like to perform? Are you thinking about a specific domain? Do you need them to train a ML model or perform other analysis? Mar 15 '20 at 23:35
• Hi @EdoardoGuerriero, we were thinking in general about curlie.org annotations, so semantic concepts. Preferably we can use this in a general way, if we have to we'd have to train a certain domain? i.e. "Politics"? So it's not about classification, but really about identifying concepts in a text, based on something like curlie.org. Mar 16 '20 at 8:52

If this is not what you want and you're interested in making your own annotations at word level, e.g. "I bought an iPhone[object]" then probably the taxonomy of curlie is too generic and you will definitely need to narrow down the scope of your annotations. Take a look at some literature of NLP tasks like Named entity recognition, it's plenty of papers. Also, if you will go down this road (creating your own annotations) a good tool is Prodigy. Consider that even at word level, annotations can defined at several levels for each document. For example, in opinion mining usually people are interested in annotating not only a chunk of words representing an opinion, but also other aspects like the target of the opinion, e.g. "[This [GPS]$$_{target}$$ really sucks]$$_{opinion}$$, it never works!". But in my experience people stop at max 3 layers of annotations, and it truly depends on the purpose for which the annotations are made in the first place.