Phrase Identification Using Machine Learning Classifier
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Solution Overview
Problem
Current systems lack an efficient method for automatically identifying meaningful phrases in machine-readable documents, which are essential for classification and further processing.
Innovation Solution
A method and system that utilize a machine learning classifier to identify phrases by numerically representing features of phrase candidates and determining whether they function as a single unit in the syntax of a sentence, employing techniques such as part-of-speech tagging and n-gram analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual phrase identification is used, then accuracy of phrase identification is improved, but productivity is worsened
Solution Approach 1:
The system uses unsupervised machine learning algorithms that automatically identify phrases without requiring manual annotation or training data. The algorithm self-adjusts parameters and identifies phrase boundaries autonomously by analyzing syntactic patterns and part-of-speech sequences, eliminating the need for human intervention while maintaining high accuracy
Solution Approach 2:
The patent replaces manual phrase identification (mechanical human analysis) with an automated computational system using natural language processing algorithms. The system substitutes human linguistic analysis with machine-based syntactic parsing, part-of-speech tagging, and pattern recognition to achieve both speed and accuracy
2Productivity
If automated phrase identification systems are implemented, then productivity is improved, but measurement precision is worsened
Solution Approach 1:
The system performs preliminary syntactic analysis including part-of-speech tagging and parse tree generation before identifying phrase boundaries. By preprocessing the document with linguistic annotations and establishing syntactic structures in advance, the algorithm creates a foundation that enables accurate phrase identification during automated processing
Solution Approach 2:
The algorithm incorporates feedback mechanisms where the parse tree structure and part-of-speech sequences inform phrase boundary decisions. The system uses the syntactic analysis results as feedback to refine phrase identification, ensuring that automated processing maintains high precision through iterative refinement based on linguistic patterns
3Measurement precision
If complex natural language processing is used, then accuracy of phrase identification is improved, but device complexity is worsened
Solution Approach 1:
The patent segments the natural language processing task into distinct modular components: tokenization, part-of-speech tagging, parse tree generation, and phrase boundary identification. Each component handles a specific aspect of linguistic analysis independently, reducing overall system complexity while maintaining comprehensive analysis capability through structured division of labor
Data Source
AI summary
A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.