Python uses spaCy to implement word segmentation

To implement word segmentation using spaCy, the following preparations are required: 1. Install Python: The first step is to install Python, which can be downloaded and installed from the official Python website. 2. Install spaCy: Install the spaCy library by using the pip command. Run the following command from the command line: pip install spacy 3. Download language model: To use spaCy's word segmentation function, you need to download the corresponding language model. You can download models in different languages, please refer to the official documentation of SpaCy for details. Run the following command from the command line to download the English model: python -m spacy download en For other languages, replace "en" with the corresponding Language code. 4. Import related class libraries: In the Python source code, you need to import space and related class libraries. Add the following import statement at the beginning of the source code: python import spacy from spacy.lang.en import English Introduction of sample data: Assuming we have an English text: "Hello, world! This is a sample sentence Next, we will implement a complete sample and provide the complete source code. The code is as follows: python import spacy from spacy.lang.en import English def tokenize_text(text): #Load Language Model spacy_english = English() #Create a word breaker tokenizer = spacy_english.tokenizer #Word segmentation of text tokens = tokenizer(text) #Return segmentation results return [token.text for token in tokens] #Text to be segmented text = "Hello, world! This is a sample sentence." #Participle tokens = tokenize_text(text) #Print segmentation results for token in tokens: print(token) In the above code, we first defined a function 'tokenize'_ Text 'to achieve word segmentation function. In the function, we use the 'English' class to load the English language model and create a word breaker. Then, we pass the text to be segmented to the segmentation device to obtain the segmentation result. Finally, we iterate through the segmentation results and print each segmentation.