Entity Identification Model Training for Incomplete Sentences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current entity identification models are not robust in predicting entities from incomplete sentences and lack accurate confidence scoring, leading to potential erroneous identifications and poor user experience in applications like textual suggestion systems.
Innovation Solution
The method involves training an entity identification model using complete sentences with known entities, where portions of the sentences are input to update the model's prediction confidence scores, allowing it to predict entities from incomplete sentences and provide robust confidence scores, and indicating when more input is needed to prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entity identification models are trained using complete sentences only, then the model can achieve basic entity identification capability, but the model lacks robustness in predicting entities from incomplete sentences and produces inaccurate confidence scores
Solution Approach 1:
The patent applies preliminary action by pre-training the entity identification model on complete sentences with known entities before deploying it for incomplete sentence prediction. This preliminary training establishes a baseline of reliable entity identification patterns that the model can then adapt to partial inputs, improving both reliability and confidence score accuracy when handling incomplete sentences.
Solution Approach 2:
The patent implements dynamics by adjusting the model's confidence scores dynamically based on the completeness of the input sentence. The system monitors whether the input is complete or incomplete and modulates the confidence scoring mechanism accordingly, allowing the model to maintain reliable predictions even when input data is partial or ambiguous.
2Ease of operation
If the model provides predictions for incomplete sentences, then user experience is improved through relevant suggestions, but erroneous identifications increase due to insufficient input context
Solution Approach 1:
The patent applies feedback by continuously monitoring the confidence scores generated for incomplete sentence predictions and using this information to adjust future predictions. When the model encounters incomplete sentences, it provides suggestions while feeding back the confidence level to refine its learning, thereby improving user experience without significantly increasing erroneous identifications through iterative refinement.
Solution Approach 2:
The patent implements parameter changes by adjusting the confidence score thresholds and prediction parameters based on input sentence completeness. For incomplete sentences, the system modifies prediction parameters to account for the reduced context, allowing it to provide useful suggestions while maintaining appropriate confidence levels and reducing the likelihood of erroneous identifications.
3Measurement precision
If the model requires complete sentences for accurate entity identification, then prediction accuracy is maintained, but the system cannot provide suggestions for incomplete sentences that users are typing
Solution Approach 1:
The patent applies partial action by training the model on complete sentences (excessive action for learning) but allowing it to make predictions on incomplete sentences (partial action for application). The model learns from the full context of complete sentences during training, then applies this knowledge partially to incomplete sentences, providing suggestions even when full context is unavailable, thus balancing accuracy with adaptability.
Solution Approach 2:
The patent uses preliminary action by pre-processing and training on complete sentences to establish accurate entity identification patterns before the model encounters incomplete sentences. This preliminary exposure to complete, well-structured data enables the model to adapt its predictions for incomplete inputs while maintaining reasonable accuracy, bridging the gap between training conditions and actual usage scenarios.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an entity identification model. In one aspect, a method includes obtaining a plurality of complete sentences that each include entity text that references a first entity; for each complete sentence in the plurality of complete sentences: providing a first portion of the complete sentence as input to an entity identification model that determines a predicted entity for the first portion of the complete sentence, the first portion being less than all of the complete sentence; comparing the predicted entity to the first entity; and updating the entity identification model based on the comparison of the predicted entity to the first entity.