def shallowParsing():
input_str = "A black television and a white stove were bought for the new apartment of John."
result = TextBlob(input_str)
#print(result.tags)
#chunking
reg_exp = "NP: {<DT>?<JJ>*<NN>}"
rp = nltk.RegexpParser(reg_exp)
result = rp.parse(result.tags)
print(result)
result.draw()
# (‘and’, u’CC’), (‘a’, u’DT’), (‘white’, u’JJ’), (‘stove’, u’NN’),
# (‘were’, u’VBD’), (‘bought’, u’VBN’), (‘for’, u’IN’), (‘the’, u’DT’),
# (‘new’, u’JJ’), (‘apartment’, u’NN’), (‘of’, u’IN’), (‘John’, u’NNP’)]
# The second step is chunking:
reg_exp = “NP: {<DT>?<JJ>*<NN>}”
rp = nltk.RegexpParser(reg_exp)
result = rp.parse(result.tags)
print(result)
# Output
# (S (NP A/DT black/JJ television/NN) and/CC (NP a/DT white/JJ stove/NN)
# were/VBD bought/VBN for/IN (NP the/DT new/JJ apartment/NN)of/IN John/NNP)
# It’s also possible to draw the sentence tree structure using code
result.draw()
# 7.5. Named entity recognition
# Named-entity recognition (NER) aims to find named entities in text and classify
# them into pre-defined categories (names of persons, locations, organizations, times, etc.).
# Named-entity recognition using NLTK:
from nltk import word_tokenize, pos_tag, ne_chunk
input_str = “Bill works for Apple so he went to Boston for a conference.”
print ne_chunk(pos_tag(word_tokenize(input_str)))
# Output:
# (S (PERSON Bill/NNP) works/VBZ for/IN Apple/NNP so/IN he/PRP went/VBD to/TO (GPE Boston/NNP) for/IN a/DT conference/NN ./.)
# 7.6. Coreference resolution (anaphora resolution)
def main1():
input_str=sys.argv[1]
input_str = input_str.lower()
input_str = input_str.strip()
print(input_str)
punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
no_punct = ""
for char in input_str:
if char not in punctuations:
no_punct = no_punct + char
input_str=no_punct
print(input_str)
stop_words = set(stopwords.words('english'))
tokens = word_tokenize(input_str)
input_str = [i for i in tokens if not i in stop_words]
print (input_str)
input_str2=[]
for i in range(0,len(input_str)):
l=[]
l=wordninja.split(input_str[i])
if len(l)>1:
for j in l:
input_str2.append(j)
else:
input_str2.append(l[0])
input_str=input_str2
input_str=f7(input_str)
input_str=' '.join(input_str)
nlp = spacy.load('en_core_web_sm')
sentence = input_str
doc = nlp(sentence)
for ent in doc.ents:
print(ent.text, ent.label_)
lemmatizer=WordNetLemmatizer()
input_str=word_tokenize(input_str)
print(input_str)
for i in range(len(input_str)):
input_str[i]=lemmatizer.lemmatize(input_str[i])
input_str=' '.join(input_str)
result = TextBlob(input_str)
print(result.tags)
reg_exp = "NP: {<DT>?<JJ>*<NN>}"
rp = nltk.RegexpParser(reg_exp)
result = rp.parse(result.tags)
print(result)
result.draw()
print(input_str)
return input_str
