Hello, there
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
Python >3.7
Download From HereDownload an IDE or install Jupyter notebook
To install Jupyter notebook, just open your cmd(terminal) and type pip installjupyter-notebookafter that typejupyter notebookto run it then you can see that your notebook is open athttp://127.0.0.1:8888/token.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 proceeding 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 threw 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
First, we are importing nltk , in the second line, we are importing our tokenizers sent_tokenize,word_tokenize from 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,and 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 usingPorterStemmer . 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 understood 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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