Annotate a NER dataset (for BERT)
I am working on annotating a dataset for the purpose of named entity recognition.In principle, I have seen that for multi-phrase (not single word) elements, annotations work like this (see this example below):Romania (B-CNT)United States of America (B-CNT C-CNT C-CNT C-CNT)whereB-CNTstands for “beginning-country” andC-CNTrepresents “continuing-country”.The problem that I face is that I have a case in which (not related to countries) where I need to annotate likeB-W GAP_WORD C-W C-W.How should I proceed with the annotation in this case?If I do annotate like in the schema above, should I expect aBERT-alike entity recognition system to learn and detect that a phrase can be likeB-W GAP_WORD C-W C-W, or do I need that “C-W” (continuation word) to be exactly after the B-W (beginning word)?Which solution is correct of the following 2:B-W GAP_WORD C-W C-WB-W GAP_WORD B-W C-WAnd then, in case 2, find a way to make the connection between the B-Ws (actually corresponding to the same entity)?
2021-11-23T21:55:17Z
Did you find a solution to this problem? I am working on this right now and want to label entities that are multiword. So far I have just labelled them all as individual words but its a pretty bad way to do this.
2022-10-12T09:03:57Z
Hey TG1. I am really sorry to see your message only now. I went forward with the second approach and had decent results at that time.
2024-05-29T15:54:00Z
Hi@Calin, hello@TG1it seems our problems are very similar, look:NLP: Named Entity Recognition: Location: looking for a model supporting composite city namesModelsSome city names include the state/province/country to disambiguate cities with the same name located in different regions or countries. Examples: 
   Paris, TX
   Moscow, ID
   Syracuse, NY
   Athens, United States
   Perth, GB
   Waterloo, Canada

Now there are some models capable to extract locations. I tried these (and few others): 
   Davlan/distilbert-base-multilingual-cased-ner-hrl
   dslim/bert-base-NER
   FacebookAI/xlm-roberta-large-finetuned-conll03-english

None of them can handle suc…What I want to achieve: to find a model recognizing these place:Syracuse, NY
   Athens, United Statesas ONE entity. Well,  Athens, United States (B-CNT C-CNT C-CNT) would be fine too.Very similar to your problem, isn’t it?