For NLP tasks, either you will come across these libraries or you will have to use many of these Python libraries.<\/p>\n
import nltk
\n# tokenizer
\nnltk.download("punkt")<\/p>\n
# stop words
\nnltk.download("stopwords")
\nfrom nltk.tokenize import TreebankWordTokenizer
\nfrom nltk.tokenize import WordPunctTokenizer
\nfrom nltk.tokenize import RegexpTokenizer
\nfrom nltk.tokenize import sent_tokenize
\nfrom nltk.corpus import stopwords
\nimport os.path
\nimport re<\/p>\n
from nltk.tokenize import RegexpTokenizer
\nfrom nltk.corpus import stopwords
\nfrom nltk.tokenize import RegexpTokenizer
\nimport nltk
\nfrom nltk.tokenize import TreebankWordTokenizer
\nfrom nltk.tokenize import WordPunctTokenizer
\nfrom nltk.tokenize import word_tokenize
\nfrom nltk.corpus import stopwords
\nimport nltk
\nfrom nltk.util import ngrams
\nfrom collections import Counter
\nfrom nltk.collocations import *
\nfrom nltk.tokenize import word_tokenize
\nfrom nltk.probability import ConditionalFreqDist, FreqDist
\nfrom nltk.probability import ConditionalProbDist, LaplaceProbDist
\nfrom nltk.corpus import stopwords
\nfrom nltk.metrics import TrigramAssocMeasures
\nfrom nltk.tokenize import TreebankWordTokenizer
\n#from nltk.probability import *
\nimport math
\nfrom nltk.metrics import TrigramAssocMeasures
\nfrom nltk.metrics import BigramAssocMeasures
\nfrom nltk.metrics import BigramAssocMeasures<\/p>\n
import math
\nimport random
\nfrom collections import Counter, defaultdict<\/p>\n
import nltk
\nnltk.download("gutenberg")<\/p>\n
from nltk.corpus import gutenberg
\nfrom nltk.util import ngrams<\/p>\n
import csv
\nfrom numpy import array
\nfrom numpy import asarray
\nfrom numpy import zeros
\nfrom keras.preprocessing.text import Tokenizer
\nfrom keras.preprocessing.sequence import pad_sequences
\nfrom keras.models import Sequential
\nfrom keras.layers import Dense
\nfrom keras.layers import Flatten
\nfrom keras.layers import Embedding
\nimport matplotlib.pyplot as plt<\/p>\n
import nltk<\/p>\n
nltk.download(‘averaged_perceptron_tagger’)<\/p>\n
import nltk
\nfrom nltk.corpus import treebank<\/p>\n
import nltk
\nfrom nltk.tag import StanfordNERTagger
\nfrom nltk.metrics.scores import accuracy<\/p>\n
import nltk
\nfrom nltk.corpus import treebank
\nfrom nltk.classify import maxent<\/p>\n
from __future__ import print_function, unicode_literals, division<\/p>\n
import re
\nimport itertools<\/p>\n
from six.moves import map, zip<\/p>\n
from nltk.probability import (
\nFreqDist,
\nConditionalFreqDist,
\nConditionalProbDist,
\nDictionaryProbDist,
\nDictionaryConditionalProbDist,
\nLidstoneProbDist,
\nMutableProbDist,
\nMLEProbDist,
\nRandomProbDist,
\n)
\nfrom nltk.metrics import accuracy
\nfrom nltk.util import LazyMap, unique_list
\nfrom nltk.compat import python_2_unicode_compatible
\nfrom nltk.tag.api import TaggerI<\/p>\n
import matplotlib as mpl
\nimport matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
from sklearn import datasets
\nfrom sklearn.mixture import GaussianMixture
\nfrom sklearn.model_selection import StratifiedKFold<\/p>\n
import tensorflow as tf
\nimport numpy as np
\nimport random<\/p>\n
import keras
\nfrom keras.layers import Dense
\nfrom keras import models
\nfrom keras import layers
\nfrom keras.layers import Activation, Dense<\/p>\n
from keras import optimizers<\/p>\n
from gensim.summarization import summarize<\/p>\n
from gensim.summarization import keywords
\nfrom sklearn.datasets import fetch_20newsgroups
\nfrom nltk.corpus import wordnet
\nfrom nltk.stem import WordNetLemmatizer
\nimport string
\nfrom sklearn.feature_extraction.text import CountVectorizer
\nfrom sklearn.decomposition import LatentDirichletAllocation
\nfrom sklearn.decomposition import TruncatedSVD
\nnltk.download("averaged_perceptron_tagger")
\nnltk.download(‘tagsets’)
\nnltk.help.upenn_tagset("JJS")
\nnltk.download(‘treebank’)
\nnltk.download(‘ brown’)
\nnltk.download(‘universal_tagset’)
\nimport nltk
\nfrom nltk.corpus import treebank
\nimport nltk
\nfrom nltk.corpus import treebank
\nfrom bs4 import BeautifulSoup # For HTML parsing
\nimport urllib # Website connections
\nimport re # Regular expressions
\nfrom time import sleep # To prevent overwhelming the server between connections
\nfrom collections import Counter # Keep track of our term counts
\nfrom nltk.corpus import stopwords # Filter out stopwords, such as ‘the’, ‘or’, ‘and’
\nimport pandas as pd # For converting results to a dataframe and bar chart plots
\nimport numpy as np
\nimport copy
\n%matplotlib inline
\nfrom sklearn.mixture import GaussianMixture
\nfrom sklearn.feature_extraction.text import TfidfVectorizer
\nimport operator<\/p>\n
from sklearn.datasets import load_files
\nimport nltk
\nimport string
\nfrom sklearn.feature_extraction.text import CountVectorizer
\nfrom nltk.stem import PorterStemmer
\nfrom sklearn.naive_bayes import MultinomialNB
\nfrom sklearn.metrics import precision_recall_fscore_support
\nimport pandas as pd
\nimport nltk
\nfrom nltk.corpus import treebank
\nimport tensorflow as tf
\nimport keras
\nfrom keras.layers import Dense
\nfrom keras import models
\nfrom keras import Sequential<\/p>\n
from numpy import array
\nfrom keras.preprocessing.text import one_hot
\nfrom keras.preprocessing.sequence import pad_sequences
\nfrom keras.models import Sequential
\nfrom keras.layers import Dense
\nfrom keras.layers import Flatten
\nfrom keras.layers.embeddings import Embedding
\nfrom sklearn.datasets import load_files
\nimport nltk
\nimport string
\nfrom sklearn.feature_extraction.text import CountVectorizer
\nfrom nltk.stem import PorterStemmer
\nfrom keras import models
\nfrom numpy import array
\nfrom keras.preprocessing.text import one_hot<\/p>\n
from keras.models import Sequential
\nfrom keras.layers import Dense
\nfrom keras.layers import Flatten
\nfrom keras.layers.embeddings import Embedding
\nfrom keras.preprocessing.text import one_hot<\/p>\n
from numpy import array
\nfrom numpy import asarray
\nfrom numpy import zeros
\nimport pandas as pd
\nfrom keras.preprocessing.text import Tokenizer
\nfrom keras.preprocessing.sequence import pad_sequences
\nfrom keras.models import Sequential
\nfrom keras.layers import Dense
\nfrom keras.layers import Flatten
\nfrom keras.layers import Embedding<\/p>\n
from keras.utils import to_categorical
\nfrom sklearn.model_selection import train_test_split<\/p>\n
from keras.models import Model, Input
\nfrom keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
\nfrom keras.layers.merge import add<\/p>\n
from seqeval.metrics import precision_score, recall_score, f1_score, classification_report<\/p>\n
from keras.models import Model, Input
\nfrom keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
\nfrom keras.layers.merge import add
\nfrom keras import models, layers<\/p>\n
from numpy import zeros<\/p>\n
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\nNote: Older short-notes from this site are posted on Medium: <\/strong><\/em>https:\/\/medium.com\/@SayedAhmedCanada<\/a><\/p>\n
\n<\/em>
\nSayed Ahmed<\/strong>
\n<\/em>
\nBSc. Eng. in Comp. Sc. & Eng. (BUET)<\/strong><\/em>
\nMSc. in Comp. Sc. (U of Manitoba, Canada)<\/strong><\/em>
\nMSc. in Data Science and Analytics (Ryerson University, Canada)<\/strong><\/em>
\nLinkedin<\/strong>: https:\/\/ca.linkedin.com\/in\/sayedjustetc<\/a>
\n<\/em><\/p>\n
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