chunking

chunking.py

import spacy 
sp = spacy.load("en_core_web_sm")

sentence = "Only accept truths that are absolutely evident"
#Tokenizing the sentence
sentence_token = word_tokenize(sentence)
# Tagging the tokens of the sentence
sentence_tagged = nltk.pos_tag(sentence_token)
print(sentence_tagged)
# Stablishing a pattern to give to the chunker
pattern =   r"""
                VP: {<ADJ_SIM><V_PRS>}
                VP: {<ADJ_INO><V.*>}
                VP: {<V_PRS><N_SING><V_SUB>}
                NP: {<N_SING><ADJ.*><N_SING>}
                NP: {<N.*><PRO>}
                VP: {<N_SING><V_.*>}
                VP: {<V.*>+}
                NP: {<ADJ.*>?<N.*>+ <ADJ.*>?}
                DNP: {<DET><NP>}
                PP: {<ADJ_CMPR><P>}
                PP: {<ADJ_SIM><P>}
                PP: {<P><N_SING>}
                PP: {<P>*}
                DDNP: {<NP><DNP>}
                NPP: {<PP><NP>+}
            """
# Chunking
chunker = nltk.RegexpParser(r'{}'.format(pattern))
chunker.parse(sentence_tagged)
Output = chunker.parse(sentence_tagged)
Output.draw()