Natural Language Processing

to be continued...

# regex for removing punctuation!
import re
# nltk preprocessing magic
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
# grabbing a part of speech function:
from part_of_speech import get_part_of_speech

text = "So many squids are jumping out of suitcases these days that you can barely go anywhere without seeing one burst forth from a tightly packed valise. I went to the dentist the other day, and sure enough I saw an angry one jump out of my dentist's bag within minutes of arriving. She hardly even noticed."

cleaned = re.sub('\W+', ' ', text)
tokenized = word_tokenize(cleaned)

stemmer = PorterStemmer()
stemmed = [stemmer.stem(token) for token in tokenized]

## -- CHANGE these -- ##
lemmatizer = None
lemmatized = []

print("Stemmed text:")
print("\nLemmatized text:")

Recent Posts

See All

Biotech & BioInformatics

Life itself is a technology now. Biotech has changed medicine. Contrary to common sense, perhaps, the notion that data has absolute value is simply not true. Data is only as valuable as the insight yo


DevOps is a collaboration of the development (Dev) and operations (Ops) teams with its foundation depending on providing IT automation. DevOps is an agile methodology that includes a set of practices