Semantic Embedding Text Classifier for Abstract Category Segmentation
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Solution Overview
Problem
Existing text classification methods are inadequate for classifying segments of text into semantically broad categories like 'risky behavior' or 'insurance suitability,' as they typically rely on simple keywords or document-level analysis, failing to account for contextual variations and requiring large labeled datasets for training.
Innovation Solution
The system employs a concept-based approach using semantic embeddings to classify text segments by projecting them onto abstract categories, allowing for the determination of semantic relatedness at the phrase or sentence level, and refining concepts through ongoing processing with a small seed set of words and phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If document-level classification with simple keywords is used, then classification speed is improved, but classification accuracy for semantically broad categories deteriorates
Solution Approach 1:
The patent segments text into smaller units (sentences, phrases, or spans) rather than classifying the entire document at once. This allows the system to apply semantic embeddings to localized segments, improving accuracy for broad categories while maintaining efficiency through targeted analysis of only relevant portions.
Solution Approach 2:
The patent transforms the classification approach by changing from simple keyword matching to using semantic embeddings that capture contextual meaning. This parameter change enables the system to accurately classify semantically broad categories by representing words and phrases in a continuous vector space where semantic relationships are preserved.
2Reliability
If extensive labeled training data is used, then model performance is improved, but data requirements and training time deteriorate
Solution Approach 1:
The patent leverages pre-trained semantic embedding models that have already learned rich linguistic representations from vast corpora. These models serve the classification task without requiring extensive task-specific labeled training data, as they can be fine-tuned or directly applied to the classification problem with minimal additional training.
Solution Approach 2:
The patent performs preliminary work by using pre-trained embedding models that have already captured semantic relationships from large-scale training. This preliminary action eliminates the need to retrain models from scratch on extensive labeled data, as the semantic understanding is already embedded in the pre-trained representations.
3Device complexity
If simple keyword-based categories are used, then classification simplicity is improved, but ability to capture contextual variations deteriorates
Solution Approach 1:
The patent changes from discrete keyword categories to continuous semantic embeddings that capture contextual nuances. This allows the system to handle semantically broad categories like 'risky behavior' or 'insurance suitability' by representing them as vectors that can capture subtle contextual variations while maintaining a relatively simple classification framework.
Data Source
AI summary
Systems and methods for detecting and using semantic relatedness to classify segments of digital text are disclosed. More particularly, embodiments determine the semantic relatedness of segments of text to abstract categories where the abstract categories are not defined by a single word or semantic concept. Detecting semantic relatedness includes analyzing text, embedding the text, and determining semantic relatedness to a set of concepts for a category where each concept may include a set of words/phrases embedded in a similar fashion. The text embedding can be projected onto each concept embedding and reduced to a score representing semantic relatedness. The text is classified based on the semantic relatedness.