Skip to main content

Natural Language Processing with Python NLTK part 3 - Stemming

Natural Language Processing


So this one will be about stemming. Stemming is used in NLP for various reasons Stemming is removing certain parts of the word to get the meaning of it. For example, Running when stemmed returns run, and cooking when stemmed returns cook.
from nltk.stem import PorterStemmer
from nltk.tokenize import word_tokenize

# testing with a sentence
sent = "when we run we get healthy, Running is awesome. I have ran for may miles."
myWords = word_tokenize(sent)
for w in myWords:
    print(PorterStemmer().stem(w))

print("**********Custom List************")
# Testing with several custom words
listwords = ["come", "came", "coming", "run", "running", "added", "adding"]
for w in listwords:
    print(PorterStemmer().stem(w))

The output will be as follows:


Popular posts from this blog

Natural Language Processing with Python NLTK part 5 - Chunking and Chinking

Natural Language Processing Using regular expression modifiers we can chunk out the PoS tagged words from the earlier example. The chunking is done with regular expressions defining a chunk rule. The Chinking defines what we need to exclude from the selection. Here are list of modifiers for Python: {1,3} = for digits, u expect 1-3 counts of digits, or "places" + = match 1 or more ? = match 0 or 1 repetitions. * = match 0 or MORE repetitions $ = matches at the end of string ^ = matches start of a string | = matches either/or. Example x|y = will match either x or y [] = range, or "variance" {x} = expect to see this amount of the preceding code. {x,y} = expect to see this x-y amounts of the preceding code source: https://pythonprogramming.net/regular-expressions-regex-tutorial-python-3/ Chunking import nltk from nltk.tokenize import word_tokenize # POS tagging sent = "This will be chunked. This is for Test. World is awesome. Hello world....

Natural Language Processing with Python NLTK part 1 - Tokenizer

Natural Language Processing Starting with the NLP articles first we will try the  tokenizer  in the NLTK package. Tokenizer breaks a paragraph into the relevant sub strings or sentences based on the tokenizer you used. In this I will use the Sent tokenizer, word_tokenizer and TweetTokenizer which has its specific work to do. import nltk from nltk.tokenize import sent_tokenize, word_tokenize, TweetTokenizer para = "Hello there this is the blog about NLP. In this blog I have made some posts. " \ "I can come up with new content." tweet = "#Fun night. :) Feeling crazy #TGIF" # tokenizing the paragraph into sentences and words sent = sent_tokenize(para) word = word_tokenize(para) # printing the output print ( "this paragraph has " + str(len(sent)) + " sentences and " + str(len(word)) + " words" ) # print each sentence k = 1 for i in sent: print ( "sentence ...

Natural Language Processing with Python NLTK part 6 - Named Entity Recognition

Natural Language Processing - NER Named entities are specific reference to something. As a part of recognizing text NLTK has allowed us to used the named entity recognition and recognize certain types of entities. Those types are as follows NE Type Examples ORGANIZATION Georgia-Pacific Corp. ,  WHO PERSON Eddy Bonte ,  President Obama LOCATION Murray River ,  Mount Everest DATE June ,  2008-06-29 TIME two fifty a m ,  1:30 p.m. MONEY 175 million Canadian Dollars ,  GBP 10.40 PERCENT twenty pct ,  18.75 % FACILITY Washington Monument ,  Stonehenge GPE South East Asia ,  Midlothian Source:  http://www.nltk.org/book/ch07.html Simple example on NER: import nltk from nltk.tokenize import word_tokenize, sent_tokenize para = " America is a country. John is a name. " sent = sent_tokenize(para) for s in sent: word = word_tokenize(s) tag = nltk . pos_tag(word) n...