Unstructured Text Clustering for Tagging Noise Reduction
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Solution Overview
Problem
In fields like insurance claims processing, medicine, and law, extracting insights from large volumes of unstructured textual data is inefficient due to 'domain noise' and the need for human experts to review vast amounts of irrelevant information, which is time-consuming and prone to errors.
Innovation Solution
A computerized method that uses optical character recognition (OCR), sentence splitting, domain noise reduction, hierarchical clustering, and summarization to create and summarize human-written sentences, reducing the data to be tagged and enhancing natural language processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually review vast amounts of unstructured textual data to extract insights and perform tagging, then comprehensive analysis can be achieved, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The patent segments unstructured textual data into discrete sentence units and clusters, transforming overwhelming volumes of text into manageable, analyzable components. This segmentation enables experts to review focused sentence clusters rather than entire documents, dramatically reducing review time while maintaining tagging accuracy through systematic organization of content.
Solution Approach 2:
The patent introduces automated preprocessing intermediaries (OCR engines, sentence splitters, noise reduction filters, and clustering algorithms) that stand between the raw unstructured data and human experts. These intermediaries perform initial filtering and organization, presenting only relevant sentence clusters to experts, thereby eliminating the need for manual review of irrelevant portions while preserving tagging quality.
2Reliability
If all unstructured textual data is presented to human experts for tagging, then complete coverage of all information is achieved, but the amount of data to be reviewed becomes overwhelming and inefficient
Solution Approach 1:
The patent extracts and removes domain noise and irrelevant boilerplate text from the unstructured data before presenting it to experts. By taking out non-informative sentences through noise reduction algorithms and clustering techniques, the system maintains complete coverage of relevant information for tagging while eliminating overwhelming volumes of redundant content that would reduce productivity.
Solution Approach 2:
The patent applies partial action by processing only the most relevant portions of unstructured data for expert review. Through clustering and noise reduction, it identifies and presents a subset of sentences that contain the critical information needed for accurate tagging, rather than requiring experts to review every sentence. This partial processing approach maintains tagging completeness for essential information while dramatically improving efficiency.
3Loss of information
If domain noise is included in the textual data during processing, then all original information is preserved, but the noise interferes with expert review and reduces tagging efficiency
Solution Approach 1:
The patent converts the harmful effect of domain noise into a benefit by using noise characteristics as features for clustering. Domain-specific boilerplate text and irrelevant sentences, while harmful to review ease, provide consistent patterns that clustering algorithms can identify and group together. This allows the system to preserve all original information in the input while systematically separating noise from valuable content, making review easier without losing any information.
4Adaptability or versatility
If common English language models are used to classify text, then general language understanding is achieved, but domain-specific terminology and phrases are not recognized as noise
Solution Approach 1:
The patent applies local quality by adapting the noise reduction and clustering processes to domain-specific characteristics. Instead of using a single generic language model for all text, the system employs domain-adapted preprocessing that recognizes local patterns and terminology specific to fields like insurance, medicine, or law. This enables precise identification of domain noise while maintaining versatile language understanding through customized processing pipelines for different domains.
Data Source
AI summary
A computerized method for reducing domain noise, creating and summarizing human-written sentences into clusters for efficient tagging in natural language processing comprising: receiving a typed, handwritten or printed text; implementing an optical character recognition (OCR) process on human written text to generate a digital version of the human written text; splitting the digital version of the typed, handwritten or printed text into an array of sentences, using a sentence splitter to generate a split sentence version; determining a domain of the human written text; based on the domain, implementing a domain noise reduction process on the split sentences version; hierarchically clustering the split sentences version after the domain noise reduction process; and summarizing the clustered sentences and reducing the amount of data to be tagged.