Progressive Topic Modeling for Document Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tools for analyzing large volumes of documents, such as customer reviews, are primitive and require manual filtering and sorting, necessitating prior knowledge of topics, which can lead to missed important information.
Innovation Solution
The implementation of progressive topic modeling and controlled vocabulary mechanisms to efficiently extract topics from documents, allowing for automatic analysis and identification of topic evolution, trends, and enhanced search experiences with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional filtering and sorting tools are used for document analysis, then basic content organization is achieved, but stakeholders need prior knowledge of topics and may miss important information
Solution Approach 1:
The topic modeling system automatically discovers and extracts topics from documents without requiring stakeholder input or prior knowledge. The system serves itself by autonomously identifying important themes, keywords, and patterns in the document collection, eliminating the need for users to pre-specify topics or manually filter content.
Solution Approach 2:
The patent introduces topic models as an intermediary layer between raw documents and stakeholder analysis. These topic models act as mediators that automatically organize and summarize document content, bridging the gap between unstructured text and meaningful insights without requiring direct user intervention or prior topic knowledge.
2Measurement precision
If manual review of documents is performed to identify topics, then comprehensive understanding is achieved, but time consumption increases significantly
Solution Approach 1:
The patent replaces the mechanical process of manual document review with automated computational topic modeling. Instead of human stakeholders manually reading and analyzing documents to identify topics, the system uses computer algorithms to automatically extract topics, keywords, and patterns, achieving both accuracy and efficiency.
Solution Approach 2:
The system performs preliminary topic modeling and document organization before stakeholders need to analyze the content. By pre-processing documents to extract topics and organize them into coherent structures, the system prepares the analysis work in advance, saving stakeholders significant time when they need to access and review the information.
3Reliability
If comprehensive topic modeling is performed on all documents, then complete topic coverage is achieved, but computational resources increase
Solution Approach 1:
The patent segments the document collection into smaller groups or batches for progressive topic modeling. Instead of processing all documents simultaneously, the system divides them into manageable segments, performs topic modeling on each segment, and gradually builds up the complete topic coverage. This approach maintains comprehensive topic analysis while reducing the computational burden on any single processing step.
Solution Approach 2:
The system performs partial topic modeling on document segments rather than complete modeling on all documents at once. By applying topic modeling to portions of the document collection iteratively and progressively, the system achieves comprehensive topic coverage over time while using computational resources efficiently in each processing step, avoiding the excessive resource demand of simultaneous full-document analysis.
Data Source
AI summary
A mechanism for progressive topic modeling is disclosed to facilitate document content analysis. Input documents can be sorted and divided into multiple groups. Topic modeling is performed for each group, where the topic modeling for one group is based on the generated topic model from a previous group, if available. The vocabulary used in the topic modeling process can also be updated for each group of documents. The generated topics can be presented in a user interface to facilitate a user in analyzing the documents. The topic modeling mechanism can also be utilized to enhance a document search experience by generating topics from documents contained in search results and presenting topic words to a user as suggested search terms.