Topic-Based Document Segmentation for Accurate Merchant Search
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Solution Overview
Problem
Conventional systems struggle to provide accurate and consumable search results from unstructured merchant documents, as they lack effective methods to organize and manage unstructured documents for efficient provisioning of relevant information in response to search queries.
Innovation Solution
A machine-learning-based document segmentation system that employs multiple levels of processing, including a first machine-learning model for initial topic segmentation and a second model for refining weights, to generate a final topic-segmented document, which is then stored in a knowledge graph for improved readability and consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unstructured documents are processed using conventional search systems, then search results can be provided, but the accuracy and consumability of the results deteriorate due to lack of effective organization and management
Solution Approach 1:
The patent applies segmentation by dividing unstructured documents into multiple topic-based segments using machine learning models. The system segments documents at multiple levels (sentence level and topic level) to organize content into coherent sections, improving search result accuracy without requiring complete manual reorganization of entire documents.
Solution Approach 2:
The patent introduces an intermediary machine learning-based segmentation system between the unstructured documents and the search results. This intermediary processes documents through multiple ML models that generate topic segmentations, acting as a mediator that transforms raw unstructured data into organized, consumable search results.
2Measurement precision
If multiple levels of machine-learning processing are applied to segment documents, then segmentation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the processing task itself into multiple independent ML model stages. Instead of using one complex model, it applies multiple specialized models at different levels (sentence segmentation, topic segmentation), which can be processed in parallel and improve accuracy without requiring a single monolithic processing step.
Solution Approach 2:
The patent performs preliminary sentence-level segmentation before topic-level segmentation. This preliminary action breaks down the complex task of document segmentation into smaller, more manageable steps, allowing each ML model to focus on specific aspects and improving overall efficiency and accuracy.
Data Source
AI summary
A system and method to identify a document including text relating to a merchant system. The document is segmented into a set of sentences. A first machine-learning model executed by a processing device generates an initial topic segmentation corresponding to the set of sentences. A second machine-learning model is applied to the initial topic segmentation to generate a final topic segmentation corresponding to the document.