class NNOTagger (BaseTagger, object):
"""
TextBlob compatible Norsk Nynorsk POS tagger class based on the NLTK HunPos wrapper.
"""
def __init__(self, model_fn=None):
self.tokenizer = NOTokenizer()
self.tagger = HunposTagger(NNO_TAGGER_DEFAULT_MODEL_FN,
hunpos_tag_bin(), encoding='utf-8')
def tag(self, text, tokenize=True):
text = clean_input(text)
if tokenize:
text = self.tokenizer.tokenize(text)
return self.tagger.tag(text)
class NNOTagger(BaseTagger, object):
"""
TextBlob compatible Norsk Nynorsk POS tagger class based on the NLTK HunPos wrapper.
"""
def __init__(self, model_fn=None):
self.tokenizer = NOTokenizer()
self.tagger = HunposTagger(NNO_TAGGER_DEFAULT_MODEL_FN,
hunpos_tag_bin(),
encoding='utf-8')
def tag(self, text, tokenize=True):
text = clean_input(text)
if tokenize:
text = self.tokenizer.tokenize(text)
return self.tagger.tag(text)
import sys
from gensim.corpora import Dictionary
from textblob import TextBlob
from es_text_analytics.data import newsgroups
from es_text_analytics.data.dataset import download_file, default_dataset_path
from es_text_analytics.data.ndt_dataset import NDTDataset
from es_text_analytics.tokenizer import NOTokenizer
"""
Generates wordcounts from a dataset.
Stores the counts in a Gensim Dictionary text file with id, word and count as tab separated fields.
"""
NO_TOKENIZER = NOTokenizer()
def preprocess_ng(doc):
return [w.lower() for w in TextBlob(doc['msg']).words]
def preprocess_ndt(doc):
return [
w.lower()
for w in TextBlob(doc['content'], tokenizer=NO_TOKENIZER).words
]
def main():
parser = ArgumentParser()
def __init__(self, model_fn=None):
self.tokenizer = NOTokenizer()
self.tagger = HunposTagger(NNO_TAGGER_DEFAULT_MODEL_FN,
hunpos_tag_bin(), encoding='utf-8')
def test_tokenize(self):
tokenizer = NOTokenizer()
self.assertEqual(['Dette', 'er', u'vårt', 'hus', '.'],
tokenizer.tokenize(u'Dette er vårt hus.'))
def __init__(self, model_fn=None):
self.tokenizer = NOTokenizer()
self.tagger = HunposTagger(NNO_TAGGER_DEFAULT_MODEL_FN,
hunpos_tag_bin(),
encoding='utf-8')
