Text-Item Recognition Error Prioritization via Embedding Search
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Solution Overview
Problem
Text-item recognition systems, such as Named Entity Recognition (NER) systems, require significant manual effort for error analysis and annotation to improve performance, especially when system performance falls below requirements, and generating sufficient annotated text is time-consuming and expensive, especially for complex applications.
Innovation Solution
A computer-implemented method that uses a word-embedding matrix to perform nearest-neighbor searches in the embedding space, identifying neighboring tokens as potential instances for annotation, thereby reducing manual effort and focusing annotation on high-impact areas, and dynamically improving machine learning models through iterative training with selected text samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual error analysis and annotation are performed to improve text-item recognition system performance, then system precision and recall are improved, but significant manual effort and time are required
Solution Approach 1:
The system automatically identifies potential error instances and prioritizes them for annotation using self-service mechanisms. The automated error analysis component scans system outputs, identifies potential errors, and ranks them by importance, enabling the system to serve its own improvement needs without extensive manual intervention.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated computational methods. Machine learning models analyze system outputs, identify potential errors, and prioritize instances for annotation, substituting human manual effort with automated algorithms that can process data at scale and speed.
2Measurement precision
If comprehensive annotation is performed to improve system performance, then system recall is improved, but annotation cost increases
Solution Approach 1:
The system applies local quality by focusing annotation efforts on specific high-impact areas rather than uniformly annotating all instances. The prioritization component identifies and flags only those instances that will have the greatest impact on improving recall, allocating annotation resources locally to where they are most needed.
Solution Approach 2:
The system performs partial action by annotating only a prioritized subset of instances rather than all possible instances. The automated error analysis identifies and ranks instances by their potential impact, allowing the system to achieve performance improvements through annotation of a partial but strategically selected set of examples.
3Reliability
If error analysis is performed across all text items, then complete system improvement is achieved, but device complexity increases
Solution Approach 1:
The error analysis process is segmented into distinct modular components: automated error analysis, instance prioritization, and annotation. Each component handles a specific aspect of the improvement process, breaking down the complex task of comprehensive error analysis into manageable segments that can be implemented and maintained independently.
Solution Approach 2:
The system performs preliminary action by automatically analyzing errors and prioritizing instances before the actual annotation process. This preliminary error analysis and ranking step prepares the data in advance, organizing potential improvement targets by importance, which simplifies the subsequent annotation process and reduces overall system complexity.
Data Source
AI summary
A tool for managing text-item recognition systems such as NER (Named Entity Recognition) systems. The tool applies the system to a text corpus containing instances of text items, such as named entities, to be recognized by the system, and selecting from the text corpus a set of instances of text items which the system recognized. The tool tokenizes the text corpus such that each instance in the aforementioned set is encoded as a single token and processing the tokenized text via a word embedding scheme to generate a word embedding matrix. The tool, responsive to selecting a seed token corresponding to an instance in the aforementioned set, performs a nearest-neighbor search of the embedding space to identify a set of neighboring tokens for the seed token, and identifies the text corresponding to each neighboring token as a potential instance of a text item to be annotated.