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First, ensure you have spaCy installed and download the language model you want to use. For English, you can use the `en_core_web_sm` model. ```bash pip install spacy python -m spacy download en_core_web_sm ``` ### Step 2: Import spaCy and Load the Model Next, import spaCy and load the model. ```python import spacy # Load the English language model nlp = spacy.load('en_core_web_sm') ``` ### Step 3: Customize Tokenization Rules spaCy allows you to customize tokenization rules using the `Tokenizer` class. You can add custom rules to handle specific edge cases. ```python from spacy.tokenizer import Tokenizer # Define custom rules prefix_re = r'''^[\[\("']''' suffix_re = r'''[\]\)"']$''' infix_re = r'''[-~]''' # Create a custom tokenizer custom_tokenizer = Tokenizer(nlp.vocab, prefix_search=nlp.tokenizer.prefix_search, suffix_search=nlp.tokenizer.suffix_search, infix_finditer=nlp.tokenizer.infix_finditer, token_match=nlp.tokenizer.token_match) # Replace the default tokenizer with the custom one nlp.tokenizer = custom_tokenizer ``` ### Step 4: Tokenize Text and Visualize the Process