How to extract phrases from corpus using gensim

Question

For preprocessing the corpus I was planing to extarct common phrases from the corpus, for this I tried using Phrases model in gensim, I tried below code but it's not giving me desired output.

My code

from gensim.models import Phrases
documents = ["the mayor of new york was there", "machine learning can be useful sometimes"]

sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])

Output

[u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']

But it should come as

[u'the', u'mayor', u'of', u'new_york', u'was', u'there']

But when I tried to print vocab of train data, I can see bigram, but its not working with test data, where I am going wrong?

print bigram.vocab

defaultdict(<type 'int'>, {'useful': 1, 'was_there': 1, 'learning_can': 1, 'learning': 1, 'of_new': 1, 'can_be': 1, 'mayor': 1, 'there': 1, 'machine': 1, 'new': 1, 'was': 1, 'useful_sometimes': 1, 'be': 1, 'mayor_of': 1, 'york_was': 1, 'york': 1, 'machine_learning': 1, 'the_mayor': 1, 'new_york': 1, 'of': 1, 'sometimes': 1, 'can': 1, 'be_useful': 1, 'the': 1}) 

Answer 1

I got the solution for the problem , There was two parameters I didn't take care of it which should be passed to Phrases() model, those are

1. min_count ignore all words and bigrams with total collected count lower than this. Bydefault it value is 5

2. threshold represents a threshold for forming the phrases (higher means fewer phrases). A phrase of words a and b is accepted if (cnt(a, b) - min_count) * N / (cnt(a) * cnt(b)) > threshold, where N is the total vocabulary size. Bydefault it value is 10.0

With my above train data with two statements, threshold value was 0, so I change train datasets and add those two parameters.

My New code

from gensim.models import Phrases
documents = ["the mayor of new york was there", "machine learning can be useful sometimes","new york mayor was present"]

sentence_stream = [doc.split(" ") for doc in documents]
bigram = Phrases(sentence_stream, min_count=1, threshold=2)
sent = [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there']
print(bigram[sent])

Output

[u'the', u'mayor', u'of', u'new_york', u'was', u'there']

Gensim is really awesome :)