Progressive Topic Modeling for Document Analysis
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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
1Measurement precision
If manual filtering and sorting tools are used for document analysis, then stakeholders can examine documents based on known topics, but important unknown topics are missed and analysis efficiency is low
Solution Approach 1:
The system performs self-service by automatically discovering topics through probabilistic topic modeling without requiring stakeholder intervention or prior knowledge. The algorithm autonomously identifies hidden semantic structures and topics in the document collection, eliminating the need for manual topic specification while maintaining high accuracy in topic identification.
Solution Approach 2:
The patent replaces manual mechanical filtering and sorting operations with automated probabilistic topic modeling. Instead of stakeholders manually examining and categorizing documents, the system uses statistical algorithms to automatically extract topics, substituting human cognitive effort with computational analysis that scales efficiently to large document collections.
2Measurement precision
If traditional topic modeling is applied to large document collections, then comprehensive topic coverage is achieved, but computational resources and processing time are excessive
Solution Approach 1:
The patent segments the large document collection into smaller sub-collections or batches, applying probabilistic topic modeling to each segment separately. This segmentation allows the system to process documents in manageable portions, reducing memory requirements and processing time while maintaining comprehensive topic coverage across the entire collection through aggregation of results from all segments.
Solution Approach 2:
The system applies partial action by performing topic modeling on a representative subset of documents or using iterative approaches where initial topic models are refined progressively. This allows the system to achieve sufficient topic accuracy without processing every single document in detail, reducing overall computational burden while maintaining acceptable topic identification quality.
3Measurement precision
If traditional topic modeling is applied to large document collections, then comprehensive topic coverage is achieved, but computational resources and processing time are excessive
Solution Approach 1:
The patent segments the large document collection into smaller sub-collections or batches, applying probabilistic topic modeling to each segment separately. This segmentation allows the system to process documents in manageable portions, reducing memory requirements and processing time while maintaining comprehensive topic coverage across the entire collection through aggregation of results from all segments.
Solution Approach 2:
The system applies partial action by performing topic modeling on a representative subset of documents or using iterative approaches where initial topic models are refined progressively. This allows the system to achieve sufficient topic accuracy without processing every single document in detail, reducing overall computational burden while maintaining acceptable topic identification quality.
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.