Terminology Definition Extraction Using CNN-CRF Pipeline
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Solution Overview
Problem
The high cost and labor-intensive process of manual knowledge base construction, particularly for domain-specific knowledge bases, and the need for complex machine learning algorithms and language-specific features in automatic construction methods.
Innovation Solution
A system using unsupervised word vectors, CNN, and CRF models to automatically extract terminology definitions from text without requiring feature design, allowing for minimal human intervention and language-domain independence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual knowledge base construction is used, then knowledge base quality and accuracy are improved, but construction cost and time consumption increase significantly
Solution Approach 1:
The system enables automatic knowledge base construction by having the computational system process and extract terminology definitions from unstructured text sources without requiring manual intervention from domain experts, thus transforming a manual service into an automated self-service process
Solution Approach 2:
The patent replaces the manual mechanical process of expert annotation and knowledge base construction with an automated computational system using deep neural networks, specifically a CNN-CRF pipeline that processes text and extracts definitions automatically
2Measurement precision
If automatic knowledge base construction uses complex machine learning algorithms and language-specific features, then extraction accuracy is improved, but system complexity and development effort increase
Solution Approach 1:
The system achieves language independence by using universal deep neural network architectures that can process multiple languages through shared computational layers, allowing the same model structure to function across different languages without requiring language-specific feature engineering or separate models for each language
Solution Approach 2:
The patent employs parameter-level adaptation where the model uses learnable parameters that can be fine-tuned for different languages and domains, allowing the same architectural framework to adapt to various languages and domains through parameter adjustment rather than structural redesign
3Measurement precision
If automatic knowledge base construction relies on carefully curated features, then extraction performance is improved, but feature design effort and domain adaptation requirements increase
Solution Approach 1:
The deep neural network architecture automatically learns and extracts relevant features from input text during training, eliminating the need for manual feature engineering and curating. The system self-adapts to domain-specific patterns through its learned representations, making it domain-independent while maintaining high extraction performance
Data Source
AI summary
A method of automatically generating a terminology definition knowledge base (KB) includes mapping each word in a word sequence to a real value dense vector using dense vector representations. The word sequence is then processed using a Convolutional Neural Network (CNN) model to identify whether the word sequence includes a terminology definition and to label the word sequence with a label indicating whether a terminology definition exists within the word sequence. The word sequence is then processed using a Conditional Random Field (CRF) model to identify boundaries of the terminology definition in the word sequence. The terminology definition is then extracted and added to the terminology definition KB.