The Basics Of Natural Language Processing in 10 Minutes

Do you also want to learn NLP as Quick as Possible ? Perhaps you are here because you also want to learn natural language processing as quickly as possible, like me.

Let’s start.

The first thing we need is to install some dependency

1. Python >3.7

https://www.python.org/downloads/

2. Download an IDE or install Jupyter notebook

To install Jupyter notebook, just open your cmd(terminal) and type pip install jupyter-notebook after that type jupyter notebook to run it then you can see that your notebook is open at http://127.0.0.1:8888/ token .

3. Install packages

pip install nltk

NLTK: It is a python library that can we used to perform all the NLP tasks(stemming, lemmatization, etc..)

In this blog, we are going to learn about

Tokenization
Stopwords
Stemming
Lemmatizer
WordNet
Part of speech tagging
Bag of Words

Before learning anything let’s first understand NLP.

Natural Language refers to the way we humans communicate with each other and processing is basically formatting the data in an understandable form. So we can say that NLP (Natural Language Processing) is a way that helps computers to communicate with humans in their own language.

It is one of the broadest fields in research because there is a huge amount of data out there and from that data, a big amount of data is text data. So when there is so much data available so we need some technique through which we can process the data and retrieve some useful information from it.

Now, we have an understanding of what is NLP, let’s start understanding each topic one by one.

1. Tokenization

Tokenization is the process of dividing the whole text into tokens.

It is mainly of two types:

Word Tokenizer (separated by words)
Sentence Tokenizer (separated by sentence)

import nltk
from nltk.tokenize import sent_tokenize,word_tokenize
example_text = "Hello there, how are you doing today? The weather is great today. The sky is blue. python is awsome"
print(sent_tokenize(example_text))
print(word_tokenize(example_text))

In the above code, we are importing nltk. In the second line, we are importing our tokenizers

sent_tokenize, word_tokenizfrom

library

nltk.tokenize

, then to use the tokenizer on a text we just need to pass the text as a parameter in the tokenizer.

The output will look something like this:

##sent_tokenize (Separated by sentence)
['Hello there, how are you doing today?', 'The weather is great today.', 'The sky is blue.', 'python is awsome']
##word_tokenize (Separated by words)
['Hello', 'there', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', 'today', '.', 'The', 'sky', 'is', 'blue', '.', 'python', 'is', 'awsome']

2. Stopwords

In general, stopwords are the words in any language which does not add much meaning to a sentence. In NLP, stopwords are those words which are not important in analyzing the data.

Example: he, she, hi, etc.

Our main task is to remove all the stopwords for the text to do any further processing.

There are a total of 179 stopwords in English, using NLTK we can see all the stopwords in English.

We Just need to import

stopwords

from the library

 nltk.corpus

from nltk.corpus import stopwords
print(stopwords.words('english'))
######################
######OUTPUT##########
######################
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

To remove Stopwords for a particular text.

from nltk.corpus import stopwords
text = 'he is a good boy. he is very good in coding'
text = word_tokenize(text)
text_with_no_stopwords = [word for word in text if word not in stopwords.words('english')]
text_with_no_stopwords
##########OUTPUT##########
['good', 'boy', '.', 'good', 'coding']

3. Stemming

Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma.
In simple words, we can say that stemming is the process of removing plural and adjectives from the word.

Example :

loved → love, learning →learn

In python, we can implement stemming by using

 PorterStemmer

. we can import it from the library

nltk.stem

One thing to remember from Stemming is that it works best with single words.

from nltk.stem import PorterStemmer
ps = PorterStemmer()    ## Creating an object for porterstemmer
example_words = ['earn',"earning","earned","earns"]  ##Example words
for w in example_words:
    print(ps.stem(w))    ##Using ps object stemming the word
##########OUTPUT##########
earn
earn
earn
earn
Here we can see that earning,earned and earns are stem to there lemma or root word earn.

4. Lemmatizing

Lemmatization usually refers to doing things properly with the use of vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.

In simple words lemmatization does the same work as stemming, the difference is that lemmatization returns a meaningful word.

Example:

Stemming

history → histori

Lemmatizing

history → history

It is Mostly used when designing chatbots, Q&A bots, text prediction, etc.

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer() ## Create object for lemmatizer
example_words = ['history','formality','changes']
for w in example_words:
    print(lemmatizer.lemmatize(w))
#########OUTPUT############
----Lemmatizer-----
history
formality
change
-----Stemming------
histori
formal
chang

5. WordNet

WordNet is the lexical database i.e. dictionary for the English language, specifically designed for natural language processing. We can use wordnet for finding synonyms and antonyms.

In python, we can import wordnet from

nltk.corpus

Code For Finding Synonym and antonym for a given word.

from nltk.corpus import wordnet
synonyms = []   ## Creaing an empty list for all the synonyms
antonyms =[]    ## Creaing an empty list for all the antonyms
for syn in wordnet.synsets("happy"): ## Giving word 
    for i in syn.lemmas():        ## Finding the lemma,matching 
        synonyms.append(i.name())  ## appending all the synonyms       
        if i.antonyms():
            antonyms.append(i.antonyms()[0].name()) ## antonyms
print(set(synonyms)) ## Converting them into set for unique values
print(set(antonyms))
#########OUTPUT##########
{'felicitous', 'well-chosen', 'happy', 'glad'}
{'unhappy'}

6. Part of Speech Tagging

It is a process of converting a sentence to forms — a list of words, a list of tuples (where each tuple is having a form (word, tag)). The tag in the case is a part-of-speech tag and signifies whether the word is a noun, adjective, verb, and so on.

Part of Speech Tag List

 CC coordinating conjunction
 CD cardinal digit
 DT determiner
 EX existential there (like: “there is” … think of it like “there”)
 FW foreign word
 IN preposition/subordinating conjunction
 JJ adjective ‘big’
 JJR adjective, comparative ‘bigger’
 JJS adjective, superlative ‘biggest’
 LS list marker 1)
 MD modal could, will
 NN noun, singular ‘desk’
 NNS noun plural ‘desks’
 NNP proper noun, singular ‘Harrison’
 NNPS proper noun, plural ‘Americans’
 PDT predeterminer ‘all the kids’
 POS possessive ending parent’s
 PRP personal pronoun I, he, she
 PRP possessive pronoun my, his, hers
 RB adverb very, silently,
 RBR adverb, comparative better
 RBS adverb, superlative best
 RP particle give up
 TO to go ‘to’ the store.
 UH interjection errrrrrrrm
 VB verb, base form take
 VBD verb, past tense took
 VBG verb, gerund/present participle taking
 VBN verb, past participle taken
 VBP verb, sing. present, non-3d take
 VBZ verb, 3rd person sing. present takes
 WDT wh-determiner which
 WP wh-pronoun who, what
 WP possessive wh-pronoun whose
 WRB wh-abverb where, when

In python, we can do pos tagging using

nltk.pos_tag

import nltk
nltk.download('averaged_perceptron_tagger')
sample_text = '''
An sincerity so extremity he additions. Her yet there truth merit. Mrs all projecting favourable now unpleasing. Son law garden chatty temper. Oh children provided to mr elegance marriage strongly. Off can admiration prosperous now devonshire diminution law.
'''
from nltk.tokenize import word_tokenize
words = word_tokenize(sample_text)
print(nltk.pos_tag(words))
################OUTPUT############
[('An', 'DT'), ('sincerity', 'NN'), ('so', 'RB'), ('extremity', 'NN'), ('he', 'PRP'), ('additions', 'VBZ'), ('.', '.'), ('Her', 'PRP$'), ('yet', 'RB'), ('there', 'EX'), ('truth', 'NN'), ('merit', 'NN'), ('.', '.'), ('Mrs', 'NNP'), ('all', 'DT'), ('projecting', 'VBG'), ('favourable', 'JJ'), ('now', 'RB'), ('unpleasing', 'VBG'), ('.', '.'), ('Son', 'NNP'), ('law', 'NN'), ('garden', 'NN'), ('chatty', 'JJ'), ('temper', 'NN'), ('.', '.'), ('Oh', 'UH'), ('children', 'NNS'), ('provided', 'VBD'), ('to', 'TO'), ('mr', 'VB'), ('elegance', 'NN'), ('marriage', 'NN'), ('strongly', 'RB'), ('.', '.'), ('Off', 'CC'), ('can', 'MD'), ('admiration', 'VB'), ('prosperous', 'JJ'), ('now', 'RB'), ('devonshire', 'VBP'), ('diminution', 'NN'), ('law', 'NN'), ('.', '.')]

7. Bag of words

Till now we have learned about tokenizing, stemming, and lemmatizing. All of these are the part of the text cleaning, now after cleaning the text we need to convert the text into some kind of numerical representation called vectors so that we can feed the data to a machine learning model for further processing.

For converting the data into vectors we make use of some predefined libraries in python.

Let’s see how vector representation works.

sent1 = he is a good boy
sent2 = she is a good girl
sent3 = boy and girl are good 
        |
        |
  After removal of stopwords , lematization or stemming
sent1 = good boy
sent2 = good girl
sent3 = boy girl good  
        | ### Now we will calculate the frequency for each word by
        |     calculating the occurrence of each word
word  frequency
good     3
boy      2
girl     2
         | ## Then according to their occurrence we assign o or 1 
         |    according to their occurrence in the sentence
         | ## 1 for present and 0 fot not present
         f1  f2   f3
        girl good boy   
sent1    0    1    1     
sent2    1    0    1
sent3    1    1    1
### After this we pass the vector form to machine learning model

The above process can be done using a CountVectorizer in python, we can import the same from sklearn.feature_extraction.text.

CODE to implement CountVectorizer In python

import pandas as pd
sent = pd.DataFrame(['he is a good boy', 'she is a good girl', 'boy and girl are good'],columns=['text'])
corpus = []
for i in range(0,3):
    words = sent['text'][i]
    words  = word_tokenize(words)
    texts = [lemmatizer.lemmatize(word) for word in words if word not in set(stopwords.words('english'))]
    text = ' '.join(texts)
    corpus.append(text)
print(corpus)   #### Cleaned Data
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer() ## Creating Object for CountVectorizer
X = cv.fit_transform(corpus).toarray()
X  ## Vectorize Form 
############OUTPUT##############
['good boy', 'good girl', 'boy girl good']
array([[1, 0, 1],
       [0, 1, 1],
       [1, 1, 1]], dtype=int64)

Congratulations, Now you know the basics of NLP.

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