5. Exercise

Extend a to + GPE NER pattern

Let’s consider a travel assistant here. Write a function that uses the entity type GPE to find the desired destination of a user. The code below is capable of very simple parsing, but not able to handle sentences like I am going to a conference in Berlin.

  • Modify the code to make it work for more cases.

  • Incoporate that into your Telgram booking bot.

import spacy
nlp = spacy.load('en_core_web_sm')

# Here's the function that figures out the destination
def det_destination(doc):
    for i, token in enumerate(doc):
        if token.ent_type != 0 and token.ent_type_ == 'GPE':
            while True:
                token = token.head
                if token.text == 'to':
                    return doc[i].text
                if token.head == token:
                    return 'Failed to determine'
    return 'Failed to determine'

# Testing the det_destination function
doc = nlp(u'I am going to Berlin.')
# doc = nlp(u'I am going to the conference in Berlin.')
for token in doc:
    print(token.text, token.ent_type_, token.head)

I  going
am  going
going  going
to  going
Berlin GPE to
.  going

dest = det_destination(doc)
print('It seems the user wants a ticket to ' + dest)

It seems the user wants a ticket to Berlin