Nytimesconnections

Nytimesconnections At the core of the NYT Connections is a graph database, which stores entities and their relationships as nodes and edges, respectively. The graph database is populated using Named Entity Recognition (NER) and Relationship Extraction (RE) techniques, which are applied to NYT articles to identify entities and their relationships. NER algorithms identify named entities (e.g., people, organizations, and locations) in text, while RE algorithms extract relationships between these entities. The extracted entities and relationships are then stored in the graph database, which is built using open-source technologies such as Neo4j or Amazon Neptune. The graph database allows for efficient querying and traversal of the relationships between entities, enabling the creation of interactive visualizations that help users explore and understand the connections between different entities. The interactive visualizations are created using web development technologies such as HTML, CSS, and JavaScript. The D3.js library is often used to create dynamic and interactive visualizations, while web frameworks like React or Vue.js help structure the application and manage the user interface. The extracted entities and relationships are then stored in the graph database, which is built using open-source technologies such as Neo4j or Amazon Neptune. The graph database allows for efficient querying and traversal of the relationships between entities, enabling the creation of interactive visualizations that help users explore and understand the connections between different entities. The interactive visualizations are created using web development technologies such as HTML, CSS, and JavaScript. The D3.js library is often used to create dynamic and interactive visualizations, while web frameworks like React or Vue.js help structure the application and manage the user interface.

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