It is really desperate to train NER_CRF even for one entity.
I start training by adding sentence for sentence where NER fails and filling it with different entity values. I get the following results:
Ading just one examples infers with other examples such other examples are not recognized anymore. Also, even testing on examples which are exactly in training data fails sometimes.
I feel, I have to add every exact sentences structure to data. NER_CRF does not learn to mix context words so examples where you have two context words which are in two seperate training examples.
Even If I have all subparts of a sentence in training data and testing it on a example containing two those parts it fails…
I use as features:
["prefix5", "prefix2", "suffix3", "suffix2", "title", "upper"], ["bias", "upper", "title", "digit", "pattern"], ["prefix5", "prefix2", "suffix3", "suffix2", "title", "upper"]],