Domain-Specific Knowledgebase for NLP Review Comprehension
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Solution Overview
Problem
Natural language processing systems face challenges in performing domain-specific tasks due to the limitations of generic pre-trained models, which struggle to effectively utilize domain-specific knowledgebases for improved performance.
Innovation Solution
A review comprehension system that extracts modifier and aspect pairs from input text, leverages a domain-specific knowledgebase, and uses premise embeddings to generate combined vectors for analyzing input text, enabling tasks such as sentiment classification and question answering by identifying probabilities and answer spans through processing with BERT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic pre-trained models are used for natural language processing, then the system is simple and easy to implement, but the model performance deteriorates when performing domain-specific tasks
Solution Approach 1:
The patent introduces domain-specific knowledgebases as an intermediary component between the generic pre-trained model and the domain-specific task. The knowledgebase contains domain-specific information (e.g., product attributes, service features) that enhances the model's understanding without requiring complete retraining. This mediator allows the system to maintain simplicity while improving domain-specific performance.
Solution Approach 2:
The system performs preliminary actions by pre-processing input text to extract domain-specific entities and attributes before feeding them to the pre-trained model. This includes identifying aspect terms, modifiers, and relationships that are specific to the domain, thereby preparing the input in a way that leverages both generic language understanding and domain-specific knowledge.
2Measurement precision
If domain-specific knowledgebases are integrated into the system, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent segments the natural language processing task into distinct components: text preprocessing, domain entity extraction, knowledgebase querying, and sentiment analysis. Each component handles a specific aspect of the task, allowing the system to achieve high precision through specialized processing while managing complexity through modular design. The segmentation enables independent optimization of each module.
3Productivity
If complex processing steps are added to extract and combine vectors, then the productivity improves, but the ease of operation deteriorates
Solution Approach 1:
The system implements self-service mechanisms where the text preprocessing and entity extraction components automatically identify and prepare domain-specific information without requiring manual configuration. The knowledgebase is automatically queried based on extracted entities, and vectors are automatically combined based on learned relationships. This automation maintains high productivity while reducing the operational burden on users.
Data Source
AI summary
Disclosed embodiments relate to natural language processing. Techniques can include receiving input text, extracting, from the input text, at least one modifier and aspect pair, receiving data from a knowledgebase, based on the at least one modifier and aspect pair and commonsense data, generate one or more premise embeddings, convert the input text into tokens, generating at least one vector for one or more of the tokens based on an analysis of the tokens, combine the at least one vector with the one or more premise embeddings to create at least one combined vector, and analyze the at least one combined vector wherein the analysis generates an output indicative of a feature of the input text.