def install_nltk(download_dir=None):
""" Download specific collection identifiers """
if not download_dir:
download_dir = settings.NLTK_DATA_PATH
downloader = Downloader(download_dir=download_dir)
downloader.download('punkt')
downloader.download('maxent_treebank_pos_tagger')
def __init__(self, ):
"""A method to initialize parameters"""
DEFAULT_URL = 'https://raw.githubusercontent.com/JosephSefara/AfricanWordNet/master/data/index.xml'
"""The default URL for the NLTK data server's index"""
try:
downloader = Downloader(server_index_url=DEFAULT_URL)
downloader.download('africanwordnet')
except:
raise
def __init__(self):
super(RssSkill, self).__init__('RssSkill')
self._is_reading_headlines = False
self.feeds = {}
self.cached_items = {}
self.cache_time = {}
try:
pos_tag('advance')
except LookupError:
logger.debug('Tagger not installed... Trying to download')
dler = Downloader()
if not dler.download('averaged_perceptron_tagger'):
logger.debug('Trying alternative source...')
dler = Downloader(ALT_NLTK_DATA)
dler.download('averaged_perceptron_tagger',
raise_on_error=True)
def dl_nltk():
TO_DL = ['stopwords', 'punkt']
dler = Downloader('https://pastebin.com/raw/D3TBY4Mj')
for to_dl in TO_DL:
if not nltk.download(to_dl):
print('Downloading NLTK data from alternative source...')
if not dler.download(to_dl):
print('Failed download NLTK data...')
def build_list_from_nltk(self, lang):
downloader = Downloader()
# Check if NLTK data directory exists.
if StopwordRemover.nltk_dir == None:
# Create temporary directory for download
StopwordRemover.nltk_dir = tempfile.mkdtemp(prefix='cherami')
nltk.data.path = [StopwordRemover.nltk_dir]
logger.info('NLTK data directory is "{0}"'
.format(StopwordRemover.nltk_dir))
# Check if the NLTK data has already been downloaded.
if not downloader.is_installed('stopwords'):
logger.info('Downloading NLTK stopword data...')
downloader.download('stopwords', StopwordRemover.nltk_dir, True)
logger.info('NLTK stopword data downloaded.')
for word in stopwords.words(lang):
self.stopword_list.add(word)
def build_list_from_nltk(self, lang):
downloader = Downloader()
tempdir = None
# Check if the NLTK data has already been downloaded.
if not downloader.is_installed('stopwords'):
# Create temporary directory for download
tempdir = tempfile.mkdtemp(prefix='cherami')
logger.info('Downloading NLTK stopword data into "{0}"'
'...'.format(tempdir))
downloader.download('stopwords', tempdir, True)
logger.info('NLTK stopword data downloaded.')
nltk.data.path = [tempdir]
for word in stopwords.words(lang):
self.stopword_list.add(word)
# Clean up after we're done.
if tempdir is not None:
shutil.rmtree(tempdir)
from nltk.corpus import sentiwordnet as swn
from nltk.corpus import wordnet_ic as wn_ic
from nltk.corpus.reader.wordnet import Synset as WordNetSynset
# Make sure the necessary corpora are downloaded to the local drive
for token in ("wordnet", "wordnet_ic", "sentiwordnet"):
try:
nltk.data.find("corpora/" + token)
except LookupError:
try:
nltk.download(token, quiet = True, raise_on_error = True)
except ValueError:
# Sometimes there are problems with the default index.xml URL. Then we will try this...
from nltk.downloader import Downloader as NLTKDownloader
d = NLTKDownloader("http://nltk.github.com/nltk_data/")
d.download(token, quiet = True, raise_on_error = True)
# Use the Brown corpus for calculating information content (IC)
brown_ic = wn_ic.ic('ic-brown.dat')
IC_CORPUS, IC_MAX = brown_ic, {}
for key in IC_CORPUS:
IC_MAX[key] = max(IC_CORPUS[key].values())
# This will hold the WordNet version
VERSION = wn.get_version() or "3.0"
#---------------------------------------------------------------------------------------------------
DIACRITICS = {
"a": ("á", "ä", "â", "à", "å"),
"e": ("é", "ë", "ê", "è"),
from nltk.corpus import sentiwordnet as swn
from nltk.corpus import wordnet_ic as wn_ic
from nltk.corpus.reader.wordnet import Synset as WordNetSynset
# Make sure the necessary corpora are downloaded to the local drive
for token in ("wordnet", "wordnet_ic", "sentiwordnet"):
try:
nltk.data.find("corpora/" + token)
except LookupError:
try:
nltk.download(token, quiet = True, raise_on_error = True)
except ValueError:
# Sometimes there are problems with the default index.xml URL. Then we will try this...
from nltk.downloader import Downloader as NLTKDownloader
d = NLTKDownloader("http://nltk.github.com/nltk_data/")
d.download(token, quiet = True, raise_on_error = True)
# Use the Brown corpus for calculating information content (IC)
brown_ic = wn_ic.ic('ic-brown.dat')
IC_CORPUS, IC_MAX = brown_ic, {}
for key in IC_CORPUS:
IC_MAX[key] = max(IC_CORPUS[key].values())
# This will hold the WordNet version
VERSION = wn.get_version() or "3.0"
#---------------------------------------------------------------------------------------------------
DIACRITICS = {
"a": ("á", "ä", "â", "à", "å"),
"e": ("é", "ë", "ê", "è"),
import os, nltk from nltk.downloader import Downloader from nltk.stem import WordNetLemmatizer from nltk import word_tokenize, pos_tag, map_tag nltk_packages = [ 'punkt', 'maxent_treebank_pos_tagger', 'universal_tagset', 'wordnet' ] nltk_path = os.path.dirname(os.path.realpath(__file__)) + '/nltk' nltk.data.path.append(nltk_path) nltk_dl = Downloader(download_dir = nltk_path) for package in nltk_packages: nltk_dl.download(package) primary_tags = set(['NOUN', 'VERB', 'ADJ', 'ADV', 'PRON']) processes = [ 'parts_all', 'tokens_adj', 'tokens_adv', 'tokens_all', 'tokens_dense', 'tokens_noun', 'tokens_other', 'tokens_pron', 'tokens_verb' ]
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import nltk
from nltk.downloader import Downloader
from nltk.probability import FreqDist
from nltk.probability import FreqDist
from nltk.corpus import stopwords as stopwords_corpus
from nltk import pos_tag
from nltk.util import ngrams
from nltk.collocations import BigramCollocationFinder, BigramAssocMeasures
from sklearn.metrics import f1_score
downloader = Downloader()
downloader.download("stopwords")
nltk.download('averaged_perceptron_tagger')
with open("../data/full.csv", "r") as csvfile:
reader = csv.reader(csvfile, quoting=csv.QUOTE_ALL)
documents = [t[0] for t in reader]
matplotlib.rcParams.update({'font.size': 22})
stop_words = set(stopwords_corpus.words("english"))
_non_alpha = re.compile("[^a-zA-Z ]")
def normalize(text):
"""Map a token to a canonical form, e.g. lower case it, remove non-alpha characters, etc.
