import nltk

from nltk import pos_tag

from nltk.tokenize import PunktSentenceTokenizer, word_tokenize

from nltk.corpus import state_union

# Chunking -

"""

CC coordinating conjunction

CD cardinal digit

DT determiner

EX existential there (like: "there is" ... think of it like "there exists")

FW foreign word

IN preposition/subordinating conjunction

JJ adjective 'big'

JJR adjective, comparative 'bigger'

JJS adjective, superlative 'biggest'

LS list marker 1)

MD modal could, will

NN noun, singular 'desk'

NNS noun plural 'desks'

NNP proper noun, singular 'Harrison'

NNPS proper noun, plural 'Americans'

PDT predeterminer 'all the kids'

POS possessive ending parent's

PRP personal pronoun I, he, she

PRP$ possessive pronoun my, his, hers

RB adverb very, silently,

RBR adverb, comparative better

RBS adverb, superlative best

RP particle give up

TO to go 'to' the store.

UH interjection errrrrrrrm

VB verb, base form take

VBD verb, past tense took

VBG verb, gerund/present participle taking

VBN verb, past participle taken

VBP verb, sing. present, non-3d take

VBZ verb, 3rd person sing. present takes

WDT wh-determiner which

WP wh-pronoun who, what

WP$ possessive wh-pronoun whose

WRB wh-abverb where, when

"""

# retrieving the corpus

train_text = state_union.raw('2005-GWBush.txt')

text = state_union.raw('2006-GWBush.txt')

# training the sentence tokenizer (unsupervised)

tokenizer = PunktSentenceTokenizer(train_text)

sentence = tokenizer.tokenize(text)

# tagging the tokens by word tokenizing the sentence and the using regular exp to chunk the tokens

try:

for s in sentence:

token = word_tokenize(s)

pos = pos_tag(token)

print(pos)

chunkreg = r"""Chunk: {<RB.?>*<VB.?>*<NNP>+<NN>?}"""

chunkParser = nltk.RegexpParser(chunkreg)

chunked = chunkParser.parse(pos)

chunked.draw()

except Exception as e:

print(str(e))