A way to find action verb, cognition verb, stative verb and trigger words using python

I'm trying to find a way in python to identify action verbs, cognition verbs, stative verbs in a text.

Below is my code. I found a rule of action verbs. Is it correct? I don't have any idea to find the cognition and stative verbs (rule of cognition and stative verbs). Anyone can help me to find these rules?

from __future__ import unicode_literals


import nltk
from nltk import pos_tag
from nltk.corpus import wordnet



# word tokenizeing and part-of-speech tagger
document = 'For the on-board control, the rover navigation controller should limit the speed to 5 km/h with a target speed of 10 km/h.'
tokens = [nltk.word_tokenize(sent) for sent in [document]]
postag = [nltk.pos_tag(sent) for sent in tokens][0]

# Rule for  action verbs
grammar = r"""
    NBAR:
        {<NN.*>?<VB.*><RB.*>?} #Action verbs
    AV:
        {<NBAR>}
        {<NBAR><IN><NBAR>}  # Above, connected with in/of/etc...

"""
# Chunking
cp = nltk.RegexpParser(grammar)

# the result is a tree
tree = cp.parse(postag)
print(tree)

def leaves(tree):
    for subtree in tree.subtrees(filter=lambda t: t.label() == ‘AV’):
        yield subtree.leaves()


def get_word_postag(word):
    if pos_tag([word])[0][1].startswith('J'):
        return wordnet.ADJ
    if pos_tag([word])[0][1].startswith('V'):
        return wordnet.VERB
    if pos_tag([word])[0][1].startswith('N'):
        return wordnet.NOUN
    else:
        return wordnet.NOUN


def normalise(word):
    """Normalises words to lowercase."""
    word = word.lower()
    return word


def get_terms(tree):
    for leaf in leaves(tree):
        terms = [normalise(w) for w, t in leaf]
        yield terms


terms = get_terms(tree)

features = []
for term in terms:
    _term = ''
    for word in term:
        _term += ' ' + word
    features.append(_term.strip())

print(features)

Output: limit #limit is an action verb in document

Also, how can I find trigger words at the first of the sentence(i.e. Start with a trigger word)