Add new semantic frame detection model

[alanakbik]

This PR adds a new model for detecting PropBank frame. The model is trained using the "FLERT" approach, so it is much stronger than the previous 'frame' model. We also added some training data from the universal proposition bank to improve multilingual frame detection.

Use it like this:

# load the large frame model
model = Classifier.load('frame-large')

# English sentence with the verb "return" in two different senses
sentence = Sentence("Dirk returned to Berlin to return his hat.")
model.predict(sentence)
print(sentence)

# German sentence with the verb "trug" in two different senses
sentence_de = Sentence("Dirk trug einen Koffer und trug einen Hut.")
model.predict(sentence_de)
print(sentence_de) 

This should print:

Sentence[9]: "Dirk returned to Berlin to return his hat." → ["returned"/return.01, "return"/return.02]

Sentence[9]: "Dirk trug einen Koffer und trug einen Hut." → ["trug"/carry.01, "trug"/wear.01]

The printout tells us that the verbs in both sentences are correctly disambiguated.