Document Data Structuring With Adaptive Topic Profiling
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Solution Overview
Problem
Conventional topic extraction and modeling techniques fail to accurately characterize the diverse and complex documentary data in financial services due to uniform assumptions about document structure and length, leading to ineffective clustering and topic identification.
Innovation Solution
A machine learning-based document profiling model that extracts document characteristics such as length, structure, semantic neighborhood, and topic correlations to generate profiles for different document types, allowing for improved topic extraction and reorganization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional topic extraction processes use uniform assumptions about document structure and length, then the processing is simplified and faster, but the accuracy of topic extraction deteriorates for diverse documentary data
Solution Approach 1:
The system dynamically adapts the topic extraction process by first profiling each document to determine its characteristics (length, structure, language, etc.), then selecting appropriate extraction parameters and methods based on the profile. This dynamic adaptation allows the system to maintain high processing speed while achieving accurate topic extraction for diverse document types.
Solution Approach 2:
The system changes extraction parameters based on document profiles. Different documents receive different extraction configurations - for example, short social media posts use different keyword extraction thresholds and clustering parameters compared to long financial reports. This parameter customization resolves the contradiction by optimizing both speed and accuracy for each document type.
2Device complexity
If conventional topic extraction processes make universal assumptions about standard text, then the algorithm design is simplified, but the effectiveness deteriorates when applied to diverse document types with different characteristics
Solution Approach 1:
The system segments the topic extraction process into distinct phases: document profiling, parameter selection, and topic extraction. Each phase handles specific aspects independently, making the overall system more manageable despite its complexity. The profiler segments documents into characteristic categories, enabling targeted extraction strategies for each segment type.
Solution Approach 2:
The document profile acts as an intermediary between the raw document and the topic extraction algorithm. Rather than applying a single universal algorithm directly to all documents, the profile mediates by translating document characteristics into appropriate extraction parameters, bridging the gap between diverse document types and the extraction process.
3Ease of operation
If conventional topic extraction processes cluster keywords using standard distance measures, then the clustering process is straightforward, but the accuracy deteriorates for long reference documents with multiple sections and vastly different topics
Solution Approach 1:
The system segments long reference documents into multiple sections before clustering, identifying distinct topical regions. Each section is clustered separately or with different parameters, allowing the system to handle documents with vastly different topics more accurately while maintaining operational simplicity through automated section detection.
Solution Approach 2:
For long documents with multiple topics, the system performs partial clustering on different sections rather than attempting to cluster all keywords uniformly. This partial action approach focuses computational effort on identifying dominant topics in each section, improving accuracy without requiring overly complex global clustering algorithms.
Data Source
AI summary
A method of structuring entity data using a machine learning document profiling model for improved information extraction comprises: training a learning document profiling model by applying document data and identification information on different document types to the learning document profiling model; profiling entity document data using the trained learning document profiling model; generating an entity profile using the trained learning document profiling model based on the profiled document data; selecting a subset of documents comprised in the profiled document data based on the entity profile; and tagging the selected subset of documents for further processing comprising one or more of a topic extraction process, a document processing algorithm selection process, a topic importance rating process, a knowledge graph mapping process, a document signature generation process, a document querying process, and a language derivation process.