Python uses Pattern part of speech tagging

Environmental construction: 1. Install Python: Please ensure that the Python interpreter is installed, and it is recommended to use Python version 3. x. 2. Install Pattern: Pattern is a Python Web mining module that can be used for various Natural language processing tasks, including part of speech tagging. You can install the Pattern using the following command: pip install pattern Dependent class libraries: 1. Pattern: used for part of speech tagging and other Natural language processing tasks. Dataset: The Pattern module has built-in data sets for POS tagging and other Natural language processing tasks. Sample data: The following is a sample data containing multiple sentences: python text = "Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, and a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization." The complete source code is as follows: python from pattern.en import parsetree #Sample data text = "Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, and a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization." #Perform syntactic analysis and part of speech tagging on text tree = parsetree(text) for sentence in tree: for token in sentence: print(f"{token.text}: {token.pos}") Output results: Pattern: JJ is: VBZ a: DT web: NN mining: NN module: NN ... The above example first imports the 'parsetree' function of Pattern for syntax analysis and part of speech tagging of text. Then use the 'parsetree' function to convert the text into a grammar tree, dividing the text into multiple sentences, and then label each sentence with part of speech. Finally, traverse the vocabulary in each sentence and output its text and corresponding part of speech markers.