Entrance Fee Extraction


I have some event descriptions. I want to extract the entrance fee of the event. Sometimes the entrance fee is conditional. For example, "If you enter the club before 10pm, the entrance is for free. Afterwards it is 6$."

Or another example would be: "Join our party tonight at 11pm till 5am. The entrance fee is 8$. But for girls and students it's half price."

What I want to achieve is to extract the whole phrase or sentence which tells the entrance fee + it's conditions(if available).

Note I: The texts are in German language.

Note II: Often the sentences are not complete, as they are mainly event flyers or advertisements.

I'm struggling with the Stanford NE tagger and so. Could you please shed some light on me?

Any hint is appreciated, Thanks.

What we would gladly do, is to provide one thoudand of sample documents, and the detected entrance fee chunks in each document, in order to train a model (e.g. Deep Learning Model) and then to use this trained model on new Data. This would be the perfect scenario.

Hi Askary,

have you tried to use the OpenNLP NE tagger to find "Money" named entities? This model can recognize terms that represent money. When you have tagged the terms you could extract the whole sentences that contains them to get the context.

To train a NER model you could use the Stanford NER NE Learner. This node requires a corpus and a dictionary with named entities. The entities are tagged and the tagged corpus is then used for model learning. The model is a CRF not a deep learning model.

Cheers, Kilian