Python uses spaCy to extract entity relationships

Preparation work: 1. Install spaCy: Use the pip command to install the spaCy library, for example: 'pip install space'` 2. Download the English model: SpaCy provides a trained model that can be used directly. To download the English model, you can use the command: ` Python - m space download en_ Core_ Web_ Sm` 3. Import the required class library: In the source code, we need to import the spaCy library and load the downloaded English model. Dependent class libraries: -SpaCy: High performance library for Natural language processing. By using the pre trained model, you can perform tasks such as Lexical analysis, syntactic analysis, entity recognition, etc. Dataset: SpaCy itself does not provide a specific dataset, it is a library used to process natural language text data. In this example, we do not need additional datasets. Sample data: To demonstrate entity relationship extraction, we used a simple English text: "Apple Inc. was founded by Steve Jobs and Steve Wozniak on April 1, 1976 The complete sample source code is as follows: python import spacy def extract_entity_relations(text): nlp = spacy.load("en_core_web_sm") doc = nlp(text) entities = [] relations = [] for entity in doc.ents: entities.append(entity.text) for entity in doc.ents: if entity.root.head == entity.root: relations.append(entity.root.head.text) else: relations.append(entity.text + " is " + entity.root.head.text) return entities, relations text = "Apple Inc. was founded by Steve Jobs and Steve Wozniak on April 1, 1976." entities, relations = extract_entity_relations(text) print("Entities:", entities) print("Relations:", relations) This example demonstrates how to use spaCy to extract entities and their relationships. In 'extract'_ Entity_ In the 'relationships' function, we loaded a pre trained English model and extracted entities and relationships from the text separately. Finally, we print out the extracted entity list and relationship list. We can call 'extract'_ Entity_ The 'relationships' function and passes the pending text to it to perform entity relationship extraction. For the given sample data, its output will be: Entities: ['Apple Inc.', 'Steve Jobs', 'Steve Wozniak', 'April 1, 1976'] Relations: ['Inc. is founded', 'Jobs is founded', 'Wozniak is founded', 'April 1, 1976 is founded'] Please note that this example is only a simple demonstration and may not be able to handle more complex entity relationship extraction tasks. Based on actual needs, it may be necessary to further configure and customize spaCy.