<gh_stars>0
# Natural Language Toolkit: Encoders
#
# Copyright (C) 2001-2020 NLTK Project
# Author: <NAME><<EMAIL>>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Natural Language Tool Kit Based Encoder

Given a series of text, the encoder encodes the text to form a dataframe of text vectors and has the capability to perform 
WordNet Lematizer, padding, remove stopwords, HTML tags, email addresses, numericals and custom regualr expressions. 

Example:

Input:
    
data['headline'][0]='This is a beautiful/ <day?'
data['headline'][1]='There are differences in time zones96'
0    This is a beautiful/ <day?
1    There are differences in time zones96
Output:
    
(   0  1  2  3  4
0  1  2  0  0  0
1  3  4  5  0  0, {'beautiful': 1, 'day': 2, 'difference': 3, 'time': 4, 'zone': 5})


@author: <NAME> <<EMAIL>>
"""
    	
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from tensorflow.keras.preprocessing import text
from tensorflow.keras.preprocessing.sequence import pad_sequences
from nltk.stem import WordNetLemmatizer 
import pandas as pd
import re

# Natural Language Toolkit: Encoders
#
# Copyright (C) 2001-2020 NLTK Project
# Author: <NAME><<EMAIL>>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
Natural Language Tool Kit Based Encoder

Given a series of text, the encoder encodes the text to form a dataframe of text vectors and has the capability to perform 
WordNet Lematizer, padding, remove stopwords, HTML tags, email addresses, numericals and custom regualr expressions. 

Example:

Input:
    
data['headline'][0]='This is a beautiful/ <day?'
data['headline'][1]='There are differences in time zones96'
0    This is a beautiful/ <day?
1    There are differences in time zones96
Output:
    
(   0  1  2  3  4
0  1  2  0  0  0
1  3  4  5  0  0, {'beautiful': 1, 'day': 2, 'difference': 3, 'time': 4, 'zone': 5})


@author: <NAME> <<EMAIL>>
"""
    	
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from tensorflow.keras.preprocessing import text
from tensorflow.keras.preprocessing.sequence import pad_sequences
from nltk.stem import WordNetLemmatizer 
import pandas as pd
import re
# Natural Language Toolkit: Encoders # # Copyright (C) 2001-2020 NLTK Project # Author: <NAME><<EMAIL>> # URL: <http://nltk.org/> # For license information, see LICENSE.TXT Natural Language Tool Kit Based Encoder  Given a series of text, the encoder encodes the text to form a dataframe of text vectors and has the capability to perform  WordNet Lematizer, padding, remove stopwords, HTML tags, email addresses, numericals and custom regualr expressions.   Example:  Input:      data['headline'][0]='This is a beautiful/ <day?' data['headline'][1]='There are differences in time zones96' 0    This is a beautiful/ <day? 1    There are differences in time zones96 Output:      (   0  1  2  3  4 0  1  2  0  0  0 1  3  4  5  0  0, {'beautiful': 1, 'day': 2, 'difference': 3, 'time': 4, 'zone': 5})   @author: <NAME> <<EMAIL>>