# Annotation for Argument Mining **URL:** \ **Category:** Uncategorized\ **Tags:** usage, custom, solved\ **Created:** [June 11, 2018, 11:39am UTC](https://support.prodi.gy/t/annotation-for-argument-mining/601 "2018-06-11T11:39:03Z")\ **Posts on this page:** 1\ **Showing post:** 16
**Author:** ![pvcastro](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/pvcastro/32/276_2.png) [@pvcastro](https://support.prodi.gy/u/pvcastro)\ **Post date:** [June 29, 2018, 9:24am UTC](https://support.prodi.gy/t/annotation-for-argument-mining/601/16 "2018-06-29T09:24:36Z")
Hi @sooheon. From what @honnibal told me in two other posts: > [@Segmenting examples with long spans as NERs](https://support.prodi.gy/t/segmenting-examples-with-long-spans-as-ners/652/2): > > Hi @pvcastro, As I mentioned in the last thread, I’m suspicious of using the entity recognised for these long spans. I think you should try applying sentence labels, and perhaps also marking words which are important for the category you’re interested in. Then you can use the dependency parse to find the claim boundaries. You can find documentation about the dependency parser here: [https://spacy.io/usage/linguistic-features#section-dependency-parse](https://spacy.io/usage/linguistic-features#section-dependency-parse) There should be as many spans, whether you s… > [@honnibal](#): > > Otherwise, you’re always going to have boundary errors, which will cause a sequence tagging model a lot of problems. It's actually possible to create a NER model to classify spans as entities, but since they lack patterns in both semantics and even writing (regarding embeddings and letter-casing of the words), the model would have a great problem identifying the boundaries of the spans, and even the actual classes. I'm not quite sure how to follow his suggestion to handle the boundary identification as a dependency parsing problem. --- _[View the full topic](https://support.prodi.gy/t/annotation-for-argument-mining/601)._