I’m reading some papers related to Argument Mining, and there is a considerable amount of papers handling AM as a sequence tagging and/or relation extraction problem, such as http://www.aclweb.org/anthology/P/P17/P17-1002.pdf and http://www.aclweb.org/anthology/P16-1105. In their datasets, they annotate long sequences of texts and pass them over to models based on LSTM. These annotations not only contain the information of what type of tag the token is, but also to which other sequence of tokens it would be related.
The annotation below is an example of a persuasive essay annotated with the following types of entities: Major Claim, Claim and Premise. The Claims can either be for or against Major Claims, and Premises can either support or attack claims. “For”, “Against”, “Support” and “Attack” are annotated as relations between premises and claims.
I’m interested in knowing if it’s possible to produce an annotated corpus such as the one below, using prodigy. I was going through other threads mentioning future support for relation extraction, and there were some temporary suggestions that I think are no longer supported, such as using ner.mark (which became only mark, apparently). It would be necessary to annotated longer spans of texts and link them. As an example, B-Premise:111:Support below means that the premise supports the claim span starting at line 111. This would definetely have to be a manual annotation, couldn’t be something suggested by the tool, to be approved/rejected by the annotator.
|86|From|O|
|87|this|O|
|88|point|O|
|89|of|O|
|90|view|O|
|91|,|O|
|92|I|O|
|93|firmly|O|
|94|believe|O|
|95|that|O|
|96|we|B-MajorClaim|
|97|should|I-MajorClaim|
|98|attach|I-MajorClaim|
|99|more|I-MajorClaim|
|100|importance|I-MajorClaim|
|101|to|I-MajorClaim|
|102|cooperation|I-MajorClaim|
|103|during|I-MajorClaim|
|104|primary|I-MajorClaim|
|105|education|I-MajorClaim|
|106|.|O|
|107|First|O|
|108|of|O|
|109|all|O|
|110|,|O|
|111|through|B-Claim:For|
|112|cooperation|I-Claim:For|
|113|,|I-Claim:For|
|114|children|I-Claim:For|
|115|can|I-Claim:For|
|116|learn|I-Claim:For|
|117|about|I-Claim:For|
|118|interpersonal|I-Claim:For|
|119|skills|I-Claim:For|
|120|which|I-Claim:For|
|121|are|I-Claim:For|
|122|significant|I-Claim:For|
|123|in|I-Claim:For|
|124|the|I-Claim:For|
|125|future|I-Claim:For|
|126|life|I-Claim:For|
|127|of|I-Claim:For|
|128|all|I-Claim:For|
|129|students|I-Claim:For|
|130|.|O|
|131|What|B-Premise:111:Support|
|132|we|I-Premise:111:Support|
|133|acquired|I-Premise:111:Support|
|134|from|I-Premise:111:Support|
|135|team|I-Premise:111:Support|
|136|work|I-Premise:111:Support|
|137|is|I-Premise:111:Support|
|138|not|I-Premise:111:Support|
|139|only|I-Premise:111:Support|
|140|how|I-Premise:111:Support|
|141|to|I-Premise:111:Support|
|142|achieve|I-Premise:111:Support|
|143|the|I-Premise:111:Support|
|144|same|I-Premise:111:Support|
|145|goal|I-Premise:111:Support|
|146|with|I-Premise:111:Support|
|147|others|I-Premise:111:Support|
|148|but|I-Premise:111:Support|
|149|more|I-Premise:111:Support|
|150|importantly|I-Premise:111:Support|
|151|,|I-Premise:111:Support|
|152|how|I-Premise:111:Support|
|153|to|I-Premise:111:Support|
|154|get|I-Premise:111:Support|
|155|along|I-Premise:111:Support|
|156|with|I-Premise:111:Support|
|157|others|I-Premise:111:Support|
|158|.|O|
Thanks!