Training a custom NER Model to identify entities - stanford-nlp

Question

We are using the NER models to identify entities like org, percent, money, number etc - we would like to add an entity (I don't think we can extend the models) or build another model to tag these entities ( we are looking to classify financial securities).
I have just started looking at this and have used the models available so far.
I am looking at https://nlp.stanford.edu/software/crf-faq.shtml#a
to get started for the custom models are there sample data files I need to look at?
Does this still mean that the only entities that can be tagged are the already available ones like organization, date, money, location ...
Are there any changes one needs to made to the java files i.e which ones would I start with to understand how the classifier works.
Basically for some text like :
2.200% Notes due October 30, 2020 the principal amount $ 1,500,000,000.00 $ 186,750.00
I'd like to tag:
<security>2.200% Notes due October 30, 2020</security> the principal amount $ 1,500,000,000.00 $ 186,750.00

Answer 1

You can train a new sequence tagger with the following format:
Joe PERSON
Smith PERSON
was O
born O
in O
California LOCATION
. O
He O
works O
for O
Apple ORGANIZATION
. O
Note it should be a \t separating the token from the tag. You can use any tag you want. The statistical tagger will then be able to apply tags it saw in the training data.
You can see the full details of the properties file you should use if you look at this file in the models jar:
edu/stanford/nlp/models/ner/english.all.3class.distsim.prop
I should note, if what you're trying to extract follows a few basic patterns, you're going to probably get better results with a rule-based approach.
Here is some documentation on rule based approaches in StanfordCoreNLP:
https://nlp.stanford.edu/software/tokensregex.html