Sentence segmentation and dependency parser - spacy

Question

I’m pretty new to python (using python 3) and spacy (and programming too). Please bear with me.
I have three questions where two are more or less the same I just can’t get it to work.
I took the “syntax specific search with spacy” (example) and tried to make different things work.
My program currently reads txt and the normal extraction
if w.lower_ != 'music':
return False
works.
My first question is: How can I get spacy to extract two words?
For example: “classical music”
With the previous mentioned snippet I can make it extract either classical or music. But if I only search for one of the words I also get results I don’t want like.
Classical – period / era
Or when I look for only music
Music – baroque, modern
The second question is: How can I get the dependencies to work?
The example dependency with:
elif w.dep_ != 'nsubj': # Is it the subject of a verb?
return False
works fine. But everything else I tried does not really work.
For example, I want to extract sentences with the word “birthday” and the dependency ‘DATE’. (so the dependency is an entity)
I got
if d.ent_type_ != ‘DATE’:
return False
To work.
So now it would look like:
def extract_information(w,d):
if w.lower_ != ‘birthday’:
return False
elif d.ent_type_ != ‘DATE’:
return False
else:
return True
Does something like this even work?
If it works the third question would be how I can filter sentences for example with a DATE. So If the sentence contains a certain word and a DATE exclude it.
Last thing maybe, I read somewhere that the dependencies are based on the “Stanford typed dependencies manual”. Is there a list which of those dependencies work with spacy?
Thank you for your patience and help :)

Answer 1

Before I get into offering some simple suggestions to your questions, have you tried using displaCy's visualiser on some of your sentences?
Using an example sentence 'John's birthday was yesterday', you'll find that within the parsed sentence, birthday and yesterday are not necessarily direct dependencies of one another. So searching based on the birthday word having a dependency of a DATE type entity, might not be yield the best of results.
Onto the first question:
A brute force method would be to look for matching subsequent words after you have parsed the sentence.
doc = nlp(u'Mary enjoys classical music.')
for (i,token) in enumerate(doc):
if (token.lower_ == 'classical') and (i != len(doc)-1):
if doc[i+1].lower_ == 'music':
print 'Target Acquired!'
If you're unsure of what enumerate does, look it up. It's the pythonic way of using python.
To questions 2 and 3, one simple (but not elegant) way of solving this is to just identify in a parsed sentence if the word 'birthday' exists and if it contains an entity of type 'DATE'.
doc = nlp(u'John\'s birthday was yesterday.')
for token in doc:
if token.lower_ == 'birthday':
for entities in doc.ents:
if entities.label_ == 'DATE':
print 'Found ya!'
As for the list of dependencies, I presume you're referring to the Part-Of-Speech tags. Check out the documentation on this page.
Good luck! Hope that helped.