Document Labeling System Semantic Unit Segmentation
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Solution Overview
Problem
Conventional techniques for identifying concept labels in documents are limited in their ability to provide meaningful and relevant representations of information, often relying on word frequency, predefined topics, and failing to handle text fragments within documents, leading to redundant and non-interpretable results.
Innovation Solution
A document labeling system that extracts semantic units from documents, computes relevance scores for concept labels, and uses a reference hierarchy to determine a final set of concept labels through a Directed Acyclic Graph (DAG) representation, ensuring semantic relevance and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques use word frequency and predefined topics for concept label identification, then the process is simple and fast, but the results are redundant and non-interpretable
Solution Approach 1:
The patent segments documents into semantic units (sentences, paragraphs, or custom segments) rather than treating documents as whole units. This segmentation allows for more granular analysis of concept labels, improving semantic relevance by identifying which specific portions of the document support each concept label, thereby resolving the contradiction between processing efficiency and measurement precision.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that computes relevance scores between semantic units and concept labels using multiple features (term frequency, semantic similarity, position weights). This intermediary scoring system acts as a mediator between simple word frequency matching and complex semantic interpretation, achieving both efficiency and precision by providing interpretable relevance scores that indicate the strength of association between concepts and document portions.
2Device complexity
If the system processes entire documents for concept label identification, then fewer processing steps are needed, but the granularity and specificity of concept labels decrease
Solution Approach 1:
The patent divides documents into multiple semantic units and processes each unit independently to identify applicable concept labels. This segmentation increases concept label specificity by determining which concepts are relevant to which specific portions of the document, rather than applying generic labels to the entire document, thereby resolving the contradiction between processing complexity and measurement precision.
Solution Approach 2:
The patent applies different processing weights and relevance calculations to different semantic units based on their local characteristics (position in document, length, content density). This local quality approach ensures that concept labels are assigned with appropriate specificity to each semantic unit's context, improving measurement precision without requiring overly complex global processing.
3Measurement precision
If relevance scores are computed for all reference concept labels, then comprehensive coverage is achieved, but computational resources and time increase
Solution Approach 1:
The patent computes relevance scores for concept labels selectively rather than exhaustively for all reference concepts. By focusing computational resources on computing scores for the most promising concept labels based on initial filtering (such as term frequency matching or semantic similarity thresholds), the system achieves sufficient relevance assessment accuracy without the excessive computational energy required to evaluate all possible concept labels, thereby resolving the contradiction between measurement precision and energy consumption.
4Measurement precision
If multiple concept labels are assigned to each semantic unit, then comprehensive representation is improved, but redundancy increases
Solution Approach 1:
The patent employs a feedback mechanism where concept labels assigned to semantic units are evaluated for redundancy through relevance score comparison and hierarchical relationship checking. The system provides feedback to filter out duplicate or overly similar concept labels, maintaining comprehensive concept coverage while eliminating redundant information, thereby resolving the contradiction between concept coverage and information redundancy.
Data Source
AI summary
Techniques are described for intelligently identifying concept labels for a set of multiple documents where the identified concept labels are representative of and semantically relevant to the information contained by the set of documents. The technique includes extracting semantic units (e.g., paragraphs) from the set of documents and determining concept labels applicable to the semantic units based on relevance scores computed for the concept labels. The technique includes determining an initial set of concept labels for the set of documents based on the applicable concept labels. The technique further includes obtaining a reference hierarchy associated with the reference set of concept labels and determining a final set of concept labels for the set of documents using a reference hierarchy, the initial set of concept labels, and the relevance scores. The technique includes outputting information identifying the final set of concept labels for the set of documents.