Hybrid Keyphrase Extraction System for Domain Adaptation
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Solution Overview
Problem
Existing keyphrase extraction methods, particularly supervised and unsupervised approaches, face limitations in adaptability across domains and user interaction, with supervised methods degrading in new domains and unsupervised methods lacking accuracy in specific use cases.
Innovation Solution
A hybrid system combining supervised and unsupervised techniques using a cluster computing network with a text processing engine, candidate identification engine, graph-based ranking, and phrase embedding to select and rank keyphrases, allowing for domain adaptation and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised keyphrase extraction algorithms are used, then accuracy in specific domains is improved, but adaptability to new domains deteriorates
Solution Approach 1:
The patent combines supervised and unsupervised keyphrase extraction algorithms into a hybrid system. The supervised component (using features like TF-IDF, position, length) provides accuracy for specific domains, while the unsupervised component (using graph-based PageRank) provides domain adaptability. The system merges results from both approaches, weighting them to achieve both precision and versatility across different domains.
2Adaptability or versatility
If unsupervised keyphrase extraction algorithms are used, then domain versatility is improved, but extraction accuracy deteriorates
Solution Approach 1:
The hybrid system merges unsupervised graph-based PageRank (which provides domain versatility) with supervised feature-based scoring (which provides extraction accuracy). The unsupervised component ensures the system works across different domains without retraining, while the supervised component ensures high accuracy in identifying true keyphrases through learned features.
3Reliability
If supervised keyphrase extraction models are trained on specific domain data, then performance on that domain is improved, but performance on other domains deteriorates
Solution Approach 1:
The system segments the keyphrase extraction task into two independent components: a supervised scorer trained on domain-specific data and an unsupervised graph-based ranker that is domain-agnostic. This segmentation allows each component to excel in its strength - the supervised scorer provides domain-specific reliability while the unsupervised ranker provides cross-domain adaptability.
4Measurement precision
If hybrid systems combining supervised and unsupervised approaches are used, then accuracy and adaptability are improved, but system complexity increases
Solution Approach 1:
The hybrid system is segmented into distinct modular components: candidate generation, supervised scoring, unsupervised graph construction, and combined ranking. Each module performs a specific function and can be independently configured. This segmentation manages complexity by making each component simpler while the overall system achieves superior performance through their coordinated interaction.
Data Source
AI summary
A system and method combining supervised and unsupervised natural language processing to extract keywords from text in natural language processing, the method includes receiving, through a processor, one or more entities through an input processing unit and converting the one or more entities into a standard document object. Further, parsing the standard document object through a text processing engine into one or more of a sentence and a token and selecting through a candidate identification engine one or more right candidates to be ranked. Further, assigning one or more scores to the one or more right candidates, ranking the one or more right candidates through a graph based ranking engine, creating a connected graph between the ranked one or more right candidates and assigning, through a phrase embedding engine, an edge weight to one or more edges between a right candidate and another right candidate.