Embedding-Based Text Labeling With Selective Human Verification
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Solution Overview
Problem
Natural language processing (NLP) models require frequent updates due to changing language usage and meaning, but updating them is resource-intensive, and unlabeled data is abundant yet costly to label manually.
Innovation Solution
A method for labeling user text using embedding models to predict labels, filtering non-informative segments, and presenting predicted labels to users for confirmation, reducing computational and human effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used for all user text to ensure accurate training data, then labeling accuracy is improved, but labeling time and cost increase significantly
Solution Approach 1:
The patent segments the labeling process into two distinct phases: (1) automated label prediction using embedding models for initial label assignment, and (2) selective manual verification only for uncertain or low-confidence predictions. This segmentation allows the system to leverage automated processing for high-volume, straightforward cases while reserving human expertise for edge cases, thereby reducing overall labeling time while maintaining accuracy.
Solution Approach 2:
The patent introduces an embedding model as an intermediary between the raw user text and the final labeled training data. This intermediary automatically generates initial labels and confidence scores, which then guide the selective involvement of human labelers. The embedding model acts as a filter and pre-processor, reducing the burden on human labelers to every text sample and enabling scalable labeling without sacrificing accuracy.
2Productivity
If all user text is retained for model training to maximize data utilization, then model training quality is improved, but computational resources and storage costs increase
Solution Approach 1:
The patent applies local quality by differentiating between high-value and low-value training data segments. Instead of treating all user text uniformly, the system identifies and prioritizes segments with higher informational content, diversity, or relevance to the target task. This selective retention strategy ensures that computational resources are focused on processing and storing only the most valuable training examples, improving resource efficiency while maintaining model training quality.
Solution Approach 2:
The patent implements partial action by retaining and processing only a subset of user text that meets specific quality thresholds, rather than processing excessive amounts of all available data. The embedding model and filtering mechanisms identify and discard redundant, low-quality, or irrelevant text segments, enabling the system to achieve effective model training with a optimized portion of the available data, thereby reducing computational overhead.
3Measurement precision
If embedding models are trained on large amounts of labeled data to improve label prediction accuracy, then prediction accuracy is improved, but training data preparation cost increases
Solution Approach 1:
The patent leverages copying by utilizing publicly available, pre-labeled datasets from other domains or tasks to train the embedding model. Instead of collecting and labeling proprietary data from scratch, the system copies and adapts existing labeled corpora (such as Wikipedia articles, news articles, or open-domain text collections) to pre-train the embedding model. This approach provides sufficient predictive capability while avoiding the costly and time-consuming process of creating labeled training data from scratch.
Solution Approach 2:
The patent applies preliminary action by pre-training the embedding model on large-scale public datasets before deploying it for the specific labeling task. This pre-training phase establishes a strong foundational understanding of language, semantics, and text structures, which then transfers to the target domain. By performing this data-intensive preparation work in advance using freely available resources, the system reduces the need for extensive domain-specific labeled data and simplifies the overall data preparation process.
Data Source
AI summary
A method is provided for efficiently providing sentiments or other manual labels for textual training data. The method includes using an embedding model to project acquired user text to an embedding vector in an embedding space. Distances (e.g., cosine similarities) between this embedding vector and the embedding vectors determined for a plurality of already-label user text training examples are then determined. The already-labeled user text that has the shortest distance is determined and the label thereof is prospectively applied to the acquired user text and presented to a user for approval. The user can approve the prospectively applied label, in which case the newly acquired text is added to the training data with the prospectively applied label associated therewith for later use in training a language model. Alternatively, the user can decline the prospectively applied label and apply an alternative label to the newly acquired text.