Dynamic Topic Modeling for Electronic Document Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in identifying relevant electronic documents from large collections due to lack of effective classification based on subject matter, leading to inefficiencies in retrieving documents of interest.
Innovation Solution
A method involving semantic similarity analysis to select seed texts and update weight vectors for topic modeling, allowing for biased identification of topics in electronic documents, thereby generating a topic model that reflects user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If electronic documents are stored in large collections without classification, then storage capacity is improved, but document retrieval efficiency deteriorates
Solution Approach 1:
The patent segments the unclassified document collection into multiple topic-based groups using topic modeling. Documents are divided into distinct topic clusters (e.g., sports, politics, technology) based on their content characteristics, allowing users to retrieve documents by selecting specific topics of interest rather than searching through the entire collection.
Solution Approach 2:
The patent changes the parameter of document organization from unclassified storage to classified storage based on topic distributions. By computing topic probabilities for each document and using these as classification parameters, the system transforms the retrieval process into a parameter-driven selection mechanism that improves efficiency.
2Ease of operation
If documents are classified based on existing subject matter, then organization is improved, but relevance to user-specific interests deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by allowing users to select seed texts that represent their specific interests. The topic model dynamically adjusts to user preferences by computing topic distributions based on these seed texts, transforming the static classification system into a dynamic one that adapts to individual user needs.
Solution Approach 2:
The patent applies local quality by customizing the topic model for each user based on their selected seed texts. Instead of using a uniform classification scheme for all users, the system creates user-specific topic distributions that reflect individual interests, allowing each user to receive personalized document recommendations.
3Measurement precision
If semantic similarity analysis is performed on all text strings, then topic identification accuracy is improved, but computational complexity deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing semantic similarities between seed texts and topic texts before actual document classification. This pre-processing step creates a foundation of semantic relationships that can be reused during document analysis, reducing the computational burden during the actual retrieval process.
Solution Approach 2:
The patent extracts only the necessary semantic relationships by focusing computation on seed texts and their corresponding topic texts, rather than computing similarities for all possible text string pairs. This selective extraction of semantic relationships reduces computational complexity while maintaining accuracy.
Data Source
AI summary
According to an aspect of an embodiment, operations may include obtaining multiple electronic documents and obtaining a theme text. The method may also include selecting a seed text based on a semantic similarity between the seed text and the theme text. The method may also include changing a seed weight included in a weight vector that is used in identification of topics of the multiple electronic documents. The changed seed weight may bias the identification of topics of the plurality of electronic documents in favor of the seed text as compared to one or more other text strings of the weight vector. The method may also include generating, a representation of a topic model for display to a user, the topic model may be based on the multiple electronic documents and the weight vector.