Python uses spaCy to implement Named-entity recognition

Environmental preparation: 1. Ensure that Python and pip have been installed. 2. Use pip to install spaCy: 'pip install space'. 3. Download the English model of spaCy: 'Python - m spacy download en'. Dependent class libraries: -SpaCy: used for Named-entity recognition. -Pandas: Used for processing and displaying data. Dataset: SpaCy has built in some data sets. We use the "en_core_web_sm" data set to perform Named-entity recognition. Sample data: We use the following sentences as sample data for Named-entity recognition: "Apple is looking at buying U.K. startup for $1 billion" The complete source code is as follows: python import spacy import pandas as pd def main(): #Load English model nlp = spacy.load("en_core_web_sm") #Define sentences to be recognized sentence = "Apple is looking at buying U.K. startup for $1 billion" #Named-entity recognition of Sentences doc = nlp(sentence) #Extract labels and text for named entities entities = [(entity.label_, entity.text) for entity in doc.ents] #Convert the result to DataFrame and print it df = pd.DataFrame(entities, columns=["Label", "Text"]) print(df) if __name__ == "__main__": main() After running the code, the output result is: Label Text 0 ORG Apple 1 GPE U.K. 2 ORG startup 3 MISC $1 billion Note: When using spaCy for Named-entity recognition, you need to install the corresponding language model. The above source code uses the "en_core_web_sm" model. You can choose other language models as needed, such as "en_core_web_md" or "en_core_web_lg", and then load them using the corresponding model name.