How To Parse Verbs Using Spacy

Question

I'm trying to parse verbs in a corpus and list them in dictionaries and count how many times each verb appears as a transitive, intransitive and ditransitive. I was wondering how I could use spacy to parse through the verbs and notate them as transitive, intransitive and ditransitive.

Answer 1

Here, I summarize the code from Mirith/Verb-categorizer. Basically, you can loop through VERB token and look at their children to classify them as transitive, intransitive or ditransitive. An example is as follows.

First, import spacy,

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

Suppose you have an example of tokens,

tokens = nlp('I like this dog. It is pretty good. I saw a bird. We arrived at the classroom door with only seven seconds to spare.')

You can create following function to transform VERB into new type as you want:

def check_verb(token):
    """Check verb type given spacy token"""
    if token.pos_ == 'VERB':
        indirect_object = False
        direct_object = False
        for item in token.children:
            if(item.dep_ == "iobj" or item.dep_ == "pobj"):
                indirect_object = True
            if (item.dep_ == "dobj" or item.dep_ == "dative"):
                direct_object = True
        if indirect_object and direct_object:
            return 'DITRANVERB'
        elif direct_object and not indirect_object:
            return 'TRANVERB'
        elif not direct_object and not indirect_object:
            return 'INTRANVERB'
        else:
            return 'VERB'
    else:
        return token.pos_

Example

[check_verb(t) for t in tokens] # ['PRON', 'TRAN', 'DET', 'NOUN', 'PUNCT', ...]