Dynamic Document Annotation With Neural Candidate Scoring
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Solution Overview
Problem
Manual labeling of training data for neural networks in natural language processing is time-consuming, costly, and prone to errors due to the vast amount of data required.
Innovation Solution
A system that dynamically correlates metadata to documents using a neural network to identify candidate locations and generate training labels, eliminating the need for manual labeling by transforming metadata into natural language queries and utilizing a knowledge base to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to map metadata to document locations, then labeling accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a query generator, candidate location identifier, and scorer that mediates between metadata and document locations. This automated intermediary process replaces manual labeling while maintaining accuracy through multiple processing stages including query generation, candidate identification, and scoring/ranking of candidates.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computational system. The mechanical action of human annotators manually mapping metadata to locations is substituted by an automated pipeline using query generation, candidate location identification, and machine learning-based scoring to determine optimal mappings.
2Reliability
If manual labeling is performed to ensure quality training data, then labeling reliability can be maintained, but error rates increase due to human fatigue and workload
Solution Approach 1:
The system performs self-service by automatically generating queries from metadata, identifying candidate locations, and scoring/ranking candidates without human intervention. This self-service automated pipeline eliminates human fatigue and consistency issues while maintaining reliability through systematic multi-stage processing and configurable thresholds.
Solution Approach 2:
The patent implements feedback mechanisms where candidates are scored and ranked based on their match quality, and thresholds can be configured to ensure only high-quality matches are selected. This feedback loop allows the system to automatically adjust and maintain reliability by filtering out low-confidence predictions.
3Productivity
If automated methods are used to label training data, then productivity increases, but measurement precision may decrease compared to manual labeling
Solution Approach 1:
The patent segments the labeling task into distinct components: query generation from metadata, candidate location identification, and scoring/ranking of candidates. This segmentation allows each component to be optimized independently and processed in parallel, dramatically improving productivity while maintaining precision through the collective operation of all segments.
Solution Approach 2:
The patent adds dimensional complexity by introducing multiple processing dimensions: generating multiple queries from metadata, identifying multiple candidate locations, and computing scores across multiple candidates. This multi-dimensional approach increases productivity by processing many possibilities simultaneously while maintaining accuracy through selective filtering and ranking.
Data Source
AI summary
A query is generated using metadata of a document. A set of candidate locations in the document to be annotated is identified as corresponding to the metadata. A set of scores is generated for the set of candidate locations using a neural network, where the set of scores indicate whether individual candidate locations satisfy the query. Based on the set of scores, a candidate location is annotated to generate an annotated candidate location as corresponding to the metadata.