Caching Deep Parse Structures for N-gram Parsing Efficiency
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Solution Overview
Problem
Current data sorting and retrieval processes are inefficient and resource-intensive, lacking flexibility and accuracy.
Innovation Solution
A method involving generating an n-gram model, computing tf-idf frequencies, identifying frequently occurring n-grams, and caching deep parse outputs to enable efficient parsing and reduce computational load by pre-computing and storing common structures for rapid retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data sorting and retrieval is performed using traditional methods, then complete data processing is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching deep parse structures for frequently occurring n-grams before they are needed during actual parsing operations. The system identifies frequent n-grams using TF-IDF analysis, generates their deep parse structures in advance, and stores them in a cache, so that during runtime, these pre-computed structures can be directly retrieved without performing full parsing operations.
2Productivity
If traditional data sorting methods are used, then all data is processed, but resource consumption and complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the parsing task into two distinct segments: frequent n-grams and infrequent n-grams. The system uses TF-IDF analysis to identify and separate frequently occurring n-grams from the rest of the data. Deep parse structures are generated and cached only for the frequent segment, while infrequent n-grams are processed using traditional parsing methods, thereby reducing overall computational complexity.
3Measurement precision
If full deep parsing is performed on all n-grams, then accurate parsing results are obtained, but processing time increases
Solution Approach 1:
The patent applies local quality by providing different processing qualities to different parts of the data based on their frequency characteristics. Frequent n-grams receive the high-quality treatment of pre-computed and cached deep parse structures, ensuring accuracy while enabling fast retrieval. Infrequent n-grams are processed using standard parsing methods, where full accuracy is less critical due to their lower occurrence frequency.
Data Source
AI summary
A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.