Skip to main content

Natural Language Processing with Python NLTK part 2 - Stop Words

Natural Language Processing


Stop words are the words which we ignore due to the fact that they do not generate any specific meaning to the sentence. Words like the, is, at etc. can be removed to extract the meaning of the sentence more easily. So NLTK has introduced us a stop words filter we can easily use. Let's see how it works.


from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

sent = "As you can see this is the blog of myself which is written by Anjula"

w = word_tokenize(sent)

# set English stop words
stop_words = set(stopwords.words('english'))

# list of standard stop words in English
print(stop_words)

# making empty arrays to store stop words and others
stop_words_in_sent = []
non_stop_words = []

# Loop through to get the stop words
for x in w:
    if x not in stop_words:
        non_stop_words.append(x)
    else:
        stop_words_in_sent.append(x)

# print result
print(non_stop_words)
print(stop_words_in_sent)

The code is simple as that the output will be as follows:


Labels:
Location: Sri Jayawardenepura Kotte, Sri Lanka

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....
Read more

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 ...
Read more

Design Patterns 1 : Introduction

Design Patterns : Introduction So its the holiday time and thought of starting with the Design patterns. In this post I'll talk about What are design patterns?, What good to us using them?, Why and when use them? and many more. So why wait? Lets start the journey to Design patterns. What are Design patterns? So over the years when programmers tried to build systems that can solve problems often they encountered problems that were difficult to overcome. So after finding a solution what they did was presenting it as an future guideline where other programmers when encountered the same problem can easily surpass that. These are what we called as Design Patterns. To see how it all started we have to go back in the past. History of Design Patterns? All these patterns buzz started in about 1977/79 when  Christopher Alexander  showed interest in using using pattern in architecture. That's right not in computer, in architectural ...
Read more