Python uses NLTK to convert words into their stem form or basic form

Environmental construction and preparation work: 1. Install Python: on the official website https://www.python.org/downloads/ Download and install the latest version of Python. 2. Install NLTK: Execute 'pip install nltk' on the command line to install NLTK. 3. Install dependent corpus: Execute 'import nltk' and 'nltk. download()' in the Python interactive interface to open the NLTK downloader, select the required data package for download. Dependent class libraries: -NLTK: Python library for Natural language processing. Dataset: -WordNet: a data set containing English words and their synonyms, Hyponymy and hypernymy and other information. You can use NLTK's WordNet data package by downloading it. Sample data: We use words from input sentences to demonstrate how to stem them, such as converting the word 'running' to 'run'. Complete sample explanation: In the following example, we will use NLTK's PorterStemmer class to implement stem processing. python import nltk from nltk.stem import PorterStemmer #Initialize stemming processor stemmer = PorterStemmer() #Example Input Sentence sentence = "I was running in the park" #Split sentences into words words = nltk.word_tokenize(sentence) #Perform stem processing on each word stemmed_words = [stemmer.stem(word) for word in words] #Output processed results print(stemmed_words) Output results: `['I', 'wa', 'run', 'in', 'the', 'park']` Full source code: python import nltk from nltk.stem import PorterStemmer def stem_words(sentence): #Initialize stemming processor stemmer = PorterStemmer() #Split sentences into words words = nltk.word_tokenize(sentence) #Perform stem processing on each word stemmed_words = [stemmer.stem(word) for word in words] return stemmed_words #Example Input Sentence sentence = "I was running in the park" #Stem processing of sentences stemmed_sentence = " ".join(stem_words(sentence)) #Output processed results print(stemmed_sentence) Output results: `I wa run in the park`