Recently I have been trying to train n-gram entities with Stanford Core NLP. I have followed the following tutorials - http://nlp.stanford.edu/software/crf-faq.shtml#b
With this, I am able to specify only unigram tokens and the class it belongs to. Can any one guide me through so that I can extend it to n-grams. I am trying to extract known entities like movie names from chat data set.
Please guide me through in case I have mis-interpretted the Stanford Tutorials and the same can be used for the n-gram training.
What I am stuck with is the following property
#structure of your training file; this tells the classifier
#that the word is in column 0 and the correct answer is in
#column 1
map = word=0,answer=1
Here the first column is the word (unigram) and the second column is the entity, for example
CHAPTER O
I O
Emma PERS
Woodhouse PERS
Now that I need to train known entities (say movie names) like Hulk, Titanic etc as movies, it would be easy with this approach. But in case I need to train I know what you did last summer or Baby's day out, what is the best approach ?