NLTK Text Processing Examples: Tokenization to NER
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NLTK Text Processing Examples
Experiment 4: Basic Text Preprocessing
This section demonstrates fundamental text preprocessing steps using NLTK, including tokenization, stop word removal, filtering for alphabetic tokens, and stemming.
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
from nltk.corpus import words
text = "Random sampling is a method of choosing a sample of observations from a population to make assumptions about the population"
tokens = word_tokenize(text)
stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()
# 1. Lowercase and filter for alphabetic tokens
alpha_tokens = [token.lower() for token in tokens if token.isalpha()]
# 2. Filter... Continue reading "NLTK Text Processing Examples: Tokenization to NER" »