If you have landed here, then you have been intrigued by the possibility of handing over having Textacy do the work of delivering subject-verb-object triples out of your text data. It can be done, but there are some nuances to making things happen.
One of the first issues I encountered was getting ValueErrors for nlp.maxlength when using Textacy’s built-in function, but if I used spaCy to create a spaCy doc everything was fine:
# Load the Space pipeline to be used
nlp = spacy.load('en_core_web_lg')
# Use the pipe method to feed documents
docs = list(nlp.pipe(texts_f))
# Checking to see if things worked:
docs[0]._.preview
Please note that Textacy does have a corpus object. I have not used it yet, but it looks like you could simply feed it the list of spaCy docs. It allows you to bundle metadata with the texts – I would like to see examples of how people are using it.
corpus = textacy.Corpus("en_core_web_sm", data=docs)
Spacy has built-in PoS tagging, accessing it looks like this:
for token in docs[0][0:5]:
print (token, token.tag_, token.pos_) # spacy.explain(token.tag_)
# If we want to see all the nouns used
# as subjects in the test document:
subjects = [str(item[0]) for item in SVOs]
subjects_set = set(subjects)
print(f"There are {len(subjects_set)} unique subjects out of {len(subjects)}.")
print(subjects_set)
# Get out just the first person singular triples:
for item in SVOs:
if str(item[0]) == '[i]':
print(item)
It looks like the verb “contents” – the verb phrase – contains more material than we want. If all we want is the very itself, we will need to target the last item in the verb list.
for item in SVOs:
if str(item[0]) == '[i]':
print(item[1][-1])