Unsupervised Keyphrase Extraction via Biased Random Walks
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Solution Overview
Problem
Conventional keyphrase extraction systems face challenges in flexibility, efficiency, and accuracy, particularly due to their reliance on massive supervised learning models that require significant fine-tuning, large volumes of annotated data, and struggle with scalability and transparency, limiting their ability to generalize across multiple domains and process long documents effectively.
Innovation Solution
The system employs contextual representations to guide network centrality analysis through a graph of document tokens, utilizing contextual themes and semantic information to generate biased random walks, incorporating named entity, document position, and theme contribution bias weights to improve keyphrase extraction, allowing for unsupervised learning and domain adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If massive supervised learning models are used for keyphrase extraction, then accuracy can be improved, but device complexity and computational resource requirements increase significantly
Solution Approach 1:
The patent segments the keyphrase extraction process into multiple components: graph construction from document tokens, theme identification through clustering, bias weight calculation (named entity, document position, theme contribution), and biased random walk execution. This segmentation replaces the monolithic supervised learning model with modular unsupervised components, reducing device complexity while maintaining extraction accuracy
Solution Approach 2:
The patent substitutes the mechanical supervised learning training process with an unsupervised probabilistic model (biased random walk). Instead of requiring annotated data and iterative training, the system uses graph-based representations and statistical calculations to directly extract keyphrases, eliminating the need for complex training infrastructure
2Adaptability or versatility
If supervised learning models with extensive fine-tuning are used, then domain adaptability can be improved, but loss of time and computational resources increase
Solution Approach 1:
The patent changes the fundamental parameters of the approach from supervised learning metrics to unsupervised probabilistic parameters. The biased random walk model uses bias weights (named entity, document position, theme contribution) that can be adjusted for different domains without retraining. This allows rapid domain adaptation by simply modifying weight parameters rather than performing time-consuming fine-tuning
Solution Approach 2:
The patent performs preliminary theme identification and bias weight calculation before the actual keyphrase extraction. By pre-computing theme contributions and entity biases from the document content, the system prepares domain-specific parameters in advance, enabling quick adaptation to new domains without extensive fine-tuning during deployment
3Measurement precision
If large volumes of annotated data are used for training, then model accuracy can be improved, but productivity and processing efficiency decrease
Solution Approach 1:
The patent implements self-service by having the system extract its own training signals from unannotated document data. The unsupervised clustering algorithm automatically identifies themes and computes bias weights directly from the document content, eliminating the need for external annotated datasets. This self-service approach maintains accuracy while dramatically improving processing efficiency
Solution Approach 2:
The patent extracts key structural signals (themes, entities, positional information) directly from the document content without requiring annotated examples. By taking out only the essential structural features needed for keyphrase extraction and discarding the need for labor-intensive annotation, the system achieves both high accuracy and processing efficiency
4Ease of operation
If conventional keyphrase extraction systems are used, then implementation can be simplified, but adaptability to diverse domains and document types is limited
Solution Approach 1:
The patent creates a universal keyphrase extraction system where the same graph-based biased random walk framework handles diverse domains and document types. The multi-functional bias weights (theme contribution, named entity, document position) can simultaneously capture different aspects of various document types, making the system universally applicable without domain-specific customization
Solution Approach 2:
The patent adds dimensional richness by incorporating multiple bias weight dimensions (theme, entity, position) into the extraction process. This multi-dimensional approach allows the system to adapt to diverse domains by adjusting the contribution of each dimension rather than requiring fundamentally different algorithms for different document types
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.