Multi-Word Expression Identification via Model Arbitration
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Solution Overview
Problem
Conventional dictionaries are inadequate in identifying multi-word expressions (MWEs) in natural language processing, as they only include a small percentage of MWEs and their usage varies significantly across different domains, limiting the effectiveness of noun and verb phrase recognition in natural language understanding applications.
Innovation Solution
A software and hardware facility that automatically identifies MWEs using multiple constituent models, including transformers and linguistic analysis algorithms, with a constituent model result evaluation module to arbitrate and add MWEs to a dynamic dictionary, facilitating their recognition in input text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional dictionaries are used to identify multi-word expressions, then the system is simple and easy to implement, but the identification accuracy and coverage are insufficient
Solution Approach 1:
The system segments the MWE identification task into multiple independent constituent models (e.g., statistical models, linguistic models, neural network models), each responsible for identifying specific types of MWEs. This segmentation allows each model to be optimized for its specific function while the overall system benefits from diverse perspectives, thereby improving identification accuracy without requiring a single overly complex model.
Solution Approach 2:
The system merges multiple constituent models with different approaches and strengths into a unified framework. By combining statistical models, linguistic analysis models, and neural network models, the system leverages the advantages of each approach to achieve more comprehensive and accurate MWE identification than any single model could accomplish alone.
2Measurement precision
If a comprehensive dictionary with all MWEs is created, then identification accuracy improves, but the dictionary size and maintenance complexity increase
Solution Approach 1:
The system implements self-service through automatic MWE identification and dictionary updating. The constituent models continuously analyze text data, identify new MWEs, and automatically update the dictionary without requiring manual intervention. This self-service mechanism maintains dictionary accuracy and coverage while reducing the complexity of manual maintenance.
Solution Approach 2:
The system employs feedback loops where the performance of MWE identification is continuously monitored and used to refine the constituent models and dictionary. Feedback from identification results and usage patterns allows the system to adapt and improve its MWE recognition capabilities over time, maintaining high accuracy without requiring a static, manually maintained dictionary.
3Adaptability or versatility
If multiple constituent models are used to identify MWEs, then identification comprehensiveness improves, but computing resources and processing time increase
Solution Approach 1:
The system applies partial action by selectively activating constituent models based on the specific identification task and available computational resources. Not all models need to be applied to every text processing task; the system can choose to use only the most relevant models for a given context, reducing unnecessary computational overhead while maintaining comprehensive coverage when needed.
Solution Approach 2:
The system dynamically adjusts the composition and activation of constituent models based on processing requirements, data characteristics, and resource availability. This dynamic configuration allows the system to optimize the balance between identification comprehensiveness and computational efficiency, adapting the model ensemble to match the specific needs of each processing task.
Data Source
AI summary
A facility for identifying multi-word expressions in a natural language sentence is described. The facility provides the sentence to each of multiple natural language processing modules including a first module, a second module, and a third module. Each natural language processing module uses a different approach to identify a multi-word expression and a type of the multi-word expression. Upon determining that the multiple identifiers of the multi-word expression differ, the facility determines the multi-word expression using a resolution process, which can involve a logical rule set or a machine learning model.