Computational Joke Generation System Using Keyword Segmentation
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Solution Overview
Problem
Current computational humor systems, such as AI virtual assistants, are limited in generating original, contextually integrated, and human-like jokes, often relying on prewritten content rather than creating new jokes during interactions.
Innovation Solution
A system and method that utilize databases and servers to select topic keywords from received text, generate punch words, and combine them with bridge words to create original jokes, using machine learning and natural language processing to ensure the jokes are contextually relevant and humorous.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prewritten jokes are delivered from a database, then the system can provide jokes quickly and reliably, but the jokes lack originality and contextual integration
Solution Approach 1:
The joke generation process is segmented into distinct components: topic keyword extraction from input text, bridge word selection from database, and punch word generation. This segmentation allows the system to maintain reliable database access while adding originality through dynamic combination of segments based on contextual input.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing joke components (topics, bridges, punch words) in databases organized by categories and relationships. This preliminary organization enables rapid retrieval and reliable delivery while allowing flexible recombination for originality.
2Adaptability or versatility
If joke components are selected and combined dynamically, then original and contextually integrated jokes are generated, but the system complexity increases
Solution Approach 1:
The system introduces intermediary components including a topic model generator that bridges input text and joke database, and a joke assembler that combines selected components. These intermediaries manage the complexity of dynamic combination while maintaining system organization and reliability.
Solution Approach 2:
The system employs universal databases that store multiple types of joke components (topics, bridges, punch words) with standardized organizational structures. This universality allows the same database framework to support various joke generation tasks, reducing overall system complexity despite dynamic operations.
3Adaptability or versatility
If machine learning models are used to generate punch words, then humorous and contextually relevant content is created, but computational resources and processing time increase
Solution Approach 1:
The system applies machine learning selectively rather than universally - using topic models only for keyword extraction from input text and traditional database queries for retrieving pre-stored joke components. This partial application of ML reduces computational energy while maintaining contextual relevance where most needed.
Solution Approach 2:
The system performs preliminary machine learning processing offline to train topic models and organize joke databases by categories, relationships, and contextual tags. This preliminary action shifts computational energy consumption to preparation phases, enabling faster, lower-energy operation during actual joke generation.
Data Source
AI summary
Methods for generating jokes include coupling server(s) with database(s) having words stored therein; receiving text at the server(s) from an external source communicatively coupled with the server(s) through a telecommunications network; in response to receiving the text at the server(s): selecting one or more topic keywords of the topic sentence using the server(s); generating one or more punch words with the server(s) using words stored in the database related to the topic keyword(s); adding bridges to the punch word(s), using the server(s), to generate one or more jokes; communicating a signal to a first computing device through the telecommunications network using the server(s); and in response to receiving the signal at the first computing device, displaying or speaking the joke(s) using the first computing device. Systems for generating jokes include networked computer components configured to carry out the methods. The methods/systems may also be used for recognizing jokes.