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def get_entities(sent):
## chunk 1
ent1 = ""
ent2 = ""
prv_tok_dep = "" # dependency tag of previous token in the sentence
prv_tok_text = "" # previous token in the sentence
prefix = ""
modifier = ""
#############################################################
for tok in nlp(sent):
## chunk 2
# if token is a punctuation mark then move on to the next token
if tok.dep_ != "punct":
# check: token is a compound word or not
if tok.dep_ == "compound":
prefix = tok.text
# if the previous word was also a 'compound' then add the current word to it
if prv_tok_dep == "compound":
prefix = prv_tok_text + " "+ tok.text
# check: token is a modifier or not
if tok.dep_.endswith("mod") == True:
modifier = tok.text
# if the previous word was also a 'compound' then add the current word to it
if prv_tok_dep == "compound":
modifier = prv_tok_text + " "+ tok.text
## chunk 3
if tok.dep_.find("subj") == True:
ent1 = modifier +" "+ prefix + " "+ tok.text
prefix = ""
modifier = ""
prv_tok_dep = ""
prv_tok_text = ""
## chunk 4
if tok.dep_.find("obj") == True:
ent2 = modifier +" "+ prefix +" "+ tok.text
## chunk 5
# update variables
prv_tok_dep = tok.dep_
prv_tok_text = tok.text
#############################################################
return [ent1.strip(), ent2.strip()]
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