def __init__(self, directory="",fileids=r"haaretz.bgu",myEncoding="utf-8"):
     ChunkedCorpusReader.__init__(self, directory ,fileids , str2chunktree=self.__str2BguTree,sent_tokenizer=RegexpTokenizer('\n\n', gaps=True),encoding=myEncoding)
     self._format = format

	def chunked_paras(self, fileids=None, categories=None):
		return ChunkedCorpusReader.chunked_paras(
			self, self._resolve(fileids, categories))

	def __init__(self, *args, **kwargs):
		CategorizedCorpusReader.__init__(self, kwargs)
		ChunkedCorpusReader.__init__(self, *args, **kwargs)

	def tagged_paras(self, fileids=None, categories=None, simplify_tags=False):
		return ChunkedCorpusReader.tagged_paras(
			self, self._resolve(fileids, categories), simplify_tags)

	def sents(self, fileids=None, categories=None):
		return ChunkedCorpusReader.sents(self, self._resolve(fileids, categories))

########## CHUNKED CORPUS READER ###############

###Implementing CCR
from nltk.corpus.reader import ChunkedCorpusReader
root="C:\\Users\\Matrix\\AppData\\Roaming\\nltk_data\\corpora\\cookbook\\"

reader=ChunkedCorpusReader(root,r'.*\.chunk')
#Each chunk-represented in braces is considered as a word
print reader.chunked_words()
#Each sentence will be included in a Tree()
print reader.chunked_sents()
print reader.chunked_paras()

#Getting tagged tokens for each chunk (each chunk is a word but each word is not a chunk)
print reader.chunked_words()[0].leaves()
print reader.chunked_sents()[1].leaves()
#Cant apply leaves directly to a para - but we can access a sentence of a given para.
print reader.chunked_para()[0][0].leaves()
###Implementing CCCR
from nltk.corpus.reader import ConllChunkCorpusReader
root="C:\\Users\\Matrix\\AppData\\Roaming\\nltk_data\\corpora\\cookbook\\"

reader=ConllChunkCorpusReader(root,r'.*\.iob',('NP','VP'.'PP'))
print reader.chunked_words()
print reader.chunked_sents()
print reader.iob_words()
print reader.iob_sents()

def createChunker():
    chunks = ChunkedCorpusReader('data/chunks/','text_search.pos')
    tagger_classes = [UnigramTagger, BigramTagger]
    train_chunks = chunks.chunked_sents()
    chunker = TagChunker(train_chunks, tagger_classes)
    return chunker

 def tagged_sents(self, fileids=None, categories=None):
     return ChunkedCorpusReader.tagged_sents(self,self_resolve(fileids,
         categories))