Accelerated Information Extraction via Rule-Based Pre-Annotation
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Solution Overview
Problem
Developing an information extraction system that can efficiently process large volumes of data is costly and time-consuming using conventional machine learning techniques, requiring substantial resources and months to develop high-performing language models.
Innovation Solution
An iterative approach using a rules-based model for initial information extraction, updating it based on user feedback, and then training a machine learning model to achieve acceptable performance levels, allowing for faster and less resource-intensive development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning techniques are used to develop an information extraction model, then the model performance is improved, but the development time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by using a rules-based model to pre-process and annotate documents before training the machine learning model. The rules-based model performs initial information extraction and creates annotations that can be used for training, thereby preparing the data in advance and reducing the time needed for model development.
Solution Approach 2:
The patent introduces an intermediary approach by using a rules-based model as a mediator between the raw documents and the machine learning model training process. This rules-based intermediary extracts information and creates structured annotations that facilitate subsequent ML model training, acting as a bridge that simplifies the overall process.
2Reliability
If conventional machine learning techniques are used to develop an information extraction model, then the model performance is improved, but the computational resources and cost increase significantly
Solution Approach 1:
The rules-based model performs preliminary information extraction and annotation tasks that would otherwise require computationally intensive machine learning processes. By pre-processing the data and creating structured annotations using simpler rules, the system reduces the computational burden on subsequent machine learning training.
Solution Approach 2:
The patent employs a rules-based model as a temporary, computationally efficient intermediary that can be easily implemented and replaced. This rules-based approach serves as a disposable preliminary processing layer that creates the necessary annotations without requiring the substantial computational resources of full machine learning models.
3Loss of time
If a rules-based information extraction model is used, then the development time and cost are reduced, but the recall and accuracy may be slightly reduced
Solution Approach 1:
The patent implements feedback mechanisms where the rules-based model's outputs are reviewed and refined through iterative processes. User feedback and validation are incorporated to improve the accuracy of the rules-based model, allowing it to achieve acceptable performance levels while maintaining the development time advantages.
Solution Approach 2:
The rules-based model performs preliminary annotations that serve as a foundation for improving accuracy. By pre-extracting information and creating structured data, the system enables subsequent refinement and validation processes that enhance accuracy without requiring the full computational overhead of machine learning models from the outset.
Data Source
AI summary
A computing system is configured to process a first document using an anchor rule, wherein the anchor rule identifies tokens for a domain. The computing system is further configured to identify, using the anchor rule, a first set of phrases from the first document that match the tokens. The computing system is further configured to receive a first selection from a first subset of the first set of phrases. The computing system is further configured to determine, based on the first selection, a word list, wherein the word list is a list of words ranked by rate of appearance in the first document. The computing system is further configured to process, based on the word list, a second document to extract one or more points of information from the second document.