NLP Annotator Dynamic Aggressiveness Configuration
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Solution Overview
Problem
Existing natural language processing (NLP) annotators lack the ability to dynamically configure precision and recall levels, forcing users to adapt to preconfigured settings that may not meet specific needs, leading to suboptimal performance in various domains.
Innovation Solution
A computer-implemented method for an NLP annotator that allows users to set an aggressiveness level for annotation, selecting appropriate dictionaries based on this setting to adjust precision and recall values, enabling more aggressive or conservative annotation strategies tailored to individual requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If preconfigured annotation settings are used, then the system is simple to operate, but the precision and recall levels cannot be customized to meet specific domain needs
Solution Approach 1:
The patent implements dynamic configuration of annotation aggressiveness levels, allowing users to adjust precision and recall settings based on specific needs. The system transitions from static preconfigured settings to dynamic adjustable parameters, enabling the annotator to adapt its behavior (more or less aggressive annotation) according to the task requirements while maintaining a user-friendly interface.
Solution Approach 2:
The patent changes the operational parameters of the NLP annotator by introducing an aggressiveness level parameter that controls the threshold for applying annotations. By adjusting this parameter, the system can optimize the balance between precision (accuracy of annotations) and recall (completeness of annotations), allowing users to tailor the annotator's behavior to specific domains and requirements without increasing operational complexity.
2Productivity
If a single preconfigured annotator is used, then the device complexity is low, but the performance is suboptimal for various domains
Solution Approach 1:
The patent makes the NLP annotator universal by enabling it to function effectively across multiple domains and use cases through parameter adjustment. Instead of creating separate annotators for different domains, the system provides a single annotator that can be configured with different aggressiveness levels to suit various requirements, thereby improving productivity across diverse applications while avoiding the complexity of maintaining multiple specialized annotators.
Solution Approach 2:
The patent introduces dynamic configurability to the annotator, allowing it to adapt its performance characteristics based on the specific task. The aggressiveness level parameter enables the annotator to dynamically adjust its annotation behavior, optimizing performance for different domains without requiring complex domain-specific configurations, thus improving productivity while keeping the system relatively simple.
3Measurement precision
If aggressive annotation is applied, then the recall level increases, but the precision decreases
Solution Approach 1:
The patent directly addresses the precision-recall trade-off by introducing an aggressiveness level parameter that users can adjust. By changing this parameter, the system can optimize the balance between precision (avoiding false annotations) and recall (capturing all relevant annotations). This allows users to select the appropriate level of aggressiveness based on their specific needs, whether prioritizing accuracy or completeness in their annotation tasks.
Data Source
AI summary
A computer-implemented method includes receiving, by a natural language processing (NLP) annotator, an input text that is to be annotated. The method further includes determining, by the NLP annotator, a user setting that indicates an aggressiveness level of annotation to be used to annotate the input text. The method further includes selecting, by the NLP annotator, from a plurality of dictionaries, a first dictionary based at least in part on the aggressiveness level. The method further includes generating, by the NLP annotator, annotated text of the input text based at least in part on the first dictionary. The method further includes outputting, by the NLP annotator, the annotated text.