Potential bugs with the NER model

[Zhenshan-Jin]

1. Problem with the parenthesis dependency parsing:
   The parsing result is different for different formatted input

Kentucky Fried Chicken (KFC) is one of the most famous fried chicken restaurant.

Kentucky Fried Chicken(KFC) is one of the most famous fried chicken restaurant.

We can see that by removing the space between Kentucky Fried Chicken and (KFC) the parsing result is different, which, in my opinion, is not right, like Kentucky Fried Chicken( and KFC). I guess it's probably the problem with the training data. But not sure.

2. Problem with the stability of the self-trained NER model:
   I can't really show the model result here, sorry about that. The problem is that the model is unstable, like by adding some irrelevant words to the begging or the end of the paragraph, the entity being recognized by the self-trained model would be totally changed. And also the entities recognized based on paragraph and sentences is different, which should be the case based on the Transition model described by Hannibal. I'm wondering have that issue happens before.

Thanks!

Info about spaCy
spaCy version: 2.0.3
Python version: 3.6
pre-trained Models: en_core_web_md

[ines]

Merging this with #3052. We've now added a master thread for incorrect predictions and related reports – see the issue for more details.

[lock]

This thread has been automatically locked since there has not been any recent activity after it was closed. Please open a new issue for related bugs.