Python Using SpaCy to Implement Dependency Syntactic Analysis

Environmental construction and preparation work: 1. Ensure that Python is installed, and it is recommended to use Python version 3.6 or higher. 2. Install spaCy: Run 'pip install - U spacy' from the command line. 3. Install the English model of spaCy: Run 'Python - m spacy download en' from the command line_ Core_ Web_ SM '. Dependent class libraries: -SpaCy: used for Natural language processing tasks such as syntax analysis and entity recognition. Dataset: This example uses a simple sentence as sample data. The following is a complete sample code that uses spaCy to perform dependency syntactic analysis on a given sentence: python import spacy #Load English model nlp = spacy.load('en_core_web_sm') #Sentence as sample data sentence = "spaCy is a popular library for natural language processing." #Perform dependency syntactic analysis on sentences doc = nlp(sentence) #Print dependency relationships and header words for each word for token in doc: print(token.text, token.dep_, token.head.text) Output as: spaCy ROOT is is ROOT is a det library popular amod library library attr is for prep library natural amod language language pobj for processing pobj language . punct is Source code parsing: 1. Import the space library. 2. Use the 'space. load()' method to load the English model. 3. Assign the value of the sentence to be analyzed to the variable 'sense'. 4. Use the loaded model to perform dependency syntactic analysis on sentences and return the analysis result 'doc'. 5. Use the Iterator of 'doc' to obtain the words in the analysis result one by one, and print the text, dependency and head words of each word.