print("File IDs:", files)
print("Number of files:", len(files))
print(reader.words(files[0]))
print(reader.sents(files[0]))
## Reading tagged corpora #####################
reader = TaggedCorpusReader('/Users/atul/nltk_data',
r'brown.pos',
tagset='en-brown')
reader1 = TaggedCorpusReader('/Users/atul/nltk_data',
r'brown.pos',
word_tokenizer=SpaceTokenizer())
print(reader.words())
print(reader.sents())
print(reader.tagged_words())
print(reader.tagged_sents())
print(
reader.tagged_words(tagset='universal')
) ## Mapping tags to universal format, if tagset is not correct every TAG will have UNK
## Reading chunk corpora #######
reader = ChunkedCorpusReader('/Users/atul/nltk_data',
r'treebank.chunk',
tagset='en-brown')
print(reader.chunked_words()) ## Word level structure
print(reader.chunked_sents()) ## Sentence level structure
print(reader.chunked_paras()) ## Paragraph level structure
## Reading classifed corpora ##################
## classification extracted using cat_pattern (from file name), or cat_dict or cat_file ######
from TreeTaggerWrapper import treetaggerwrapper
#1) build a TreeTagger wrapper:
tagger = treetaggerwrapper.TreeTagger(TAGLANG='en',TAGDIR='D:/Programme/TreeTagger')
#2) tag your text.
tags = tagger.TagText("This is a very short text to tag.")
#3) use the tags list... (list of string output from TreeTagger).
print tags
# Check, whether the format of the tagged postings is good for the tagged corpus reader
# p.51 NLTK Cookbook
input_directory = path
from nltk.corpus.reader import TaggedCorpusReader
reader = TaggedCorpusReader(input_directory, r'.*\.txt')
reader.words()
# ['The', 'expense', 'and', 'time', 'involved', 'are', ...]
reader.tagged_words()
reader.sents()
reader.tagged_sents()
reader.paras()
reader.tagged_paras()
# testing the import #
#import sys
import myMath
print myMath.add(4,5)
print myMath.division(4, 2)
print myMath.multiply(10, 5)
print myMath.fibonacci(8)
print myMath.squareroot(48)
