Extracting Quantitative Tuples from User Posts via Event Filtering
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Solution Overview
Problem
Significant details surrounding events often go unnoticed as they are buried in the buzz surrounding the main topic, and there is no effective means to retrieve or present these details within the context of the event, leading to a loss of the true scope of the event's information.
Innovation Solution
A system and method for extracting quantitative content related to a specific event from a body of user posts, utilizing a computing system with components like a quantitative tuple generator, event filter, and quantitative value filter to identify and extract quantitative tuples from user posts, which are then organized into topical graphs for easy access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user posts are filtered and processed to extract quantitative information, then information accessibility is improved, but processing complexity increases
Solution Approach 1:
The system segments the user post processing task into distinct components: an event filter component that separates posts related to the target event from general posts, and a quantitative value filter component that extracts numerical information from relevant posts. This segmentation allows each component to specialize in one aspect of processing, improving overall information extraction efficiency while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediary filter components that act as mediators between the raw user posts and the final quantitative information extraction. The event filter serves as an intermediary that pre-processes and qualifies posts before they reach the quantitative value extraction stage, reducing the burden on subsequent processing steps and improving overall system efficiency.
2Loss of information
If all user posts are analyzed to extract quantitative data, then data completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering actions by first applying the event filter to identify and isolate user posts related to the target event before conducting quantitative value extraction. This preliminary action ensures that only relevant posts are subjected to the more time-consuming quantitative analysis, maintaining data completeness for event-related information while significantly reducing overall processing time by excluding irrelevant posts.
Solution Approach 2:
Rather than analyzing all user posts in detail, the system applies partial action by first filtering for event relevance and then applying quantitative extraction only to the subset of event-related posts. This approach achieves sufficient data completeness for the specific event context without the excessive time cost of analyzing every single user post in the corpus.
3Loss of information
If quantitative information is extracted and organized into topical graphs, then information organization is improved, but system complexity increases
Solution Approach 1:
The patent organizes extracted quantitative information into topical graphs that add a structural dimension to the data. Instead of presenting raw extracted values, the system creates graphical representations that map relationships between different quantitative facts and their contextual topics, improving information organization and accessibility while managing complexity through visual structuring.
Solution Approach 2:
The system creates simplified copies or representations of the complex processed data in the form of topical graphs. These graphs serve as accessible copies that capture the essential quantitative information and relationships without requiring users to navigate the full complexity of the underlying processing system, thereby improving information organization while effectively managing system complexity.
Data Source
AI summary
Systems and methods for extracting and organizing quantitative content with a corresponding topic in regard to a particular, desired event, from a body of user posts is presented. More particularly, nearly ubiquitously a body of user posts includes a substantial amount of highly relevant and interesting information goes largely unprocessed and widely inaccessible. According to the disclosed subject matter, a body/corpus of user posts is filtered according to a desired event such that those user posts relating to a desired event is identified. Additionally, the user posts are also filtered according to whether or not the user posts include a quantitative value. An analysis of the filtered user posts is conducted to extract, for qualifying user posts, a quantitative tuple comprising a quantitative value and a corresponding topic.