Multiple Classifiers for Attribute-Value Extraction
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Solution Overview
Problem
Current classification techniques for extracting attribute-value pairs from natural language documents are hindered by the presence of extraneous information, which degrades reliability, and no single technique is universally suited for all types of input data, limiting the effectiveness of applications like product recommendation and demand forecasting.
Innovation Solution
The use of multiple classifiers, including supervised and unsupervised/semi-supervised techniques, to label attributes and values from text, with active learning to update learning models and improve performance by leveraging the advantages of each type of technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single classification technique is used to extract attribute-value pairs from text, then the process is simple and fast, but the reliability and accuracy are degraded due to extraneous information and limitations of individual techniques
Solution Approach 1:
The patent divides the classification task into multiple segments by employing different classification techniques (supervised, unsupervised, semi-supervised) to process different portions or aspects of the text data. Each technique handles specific types of patterns, and their results are combined to produce the final classification, thereby improving reliability while managing complexity through functional segmentation
Solution Approach 2:
The patent merges multiple classification techniques into a unified system that processes text together. The supervised, unsupervised, and semi-supervised classifiers work in combination, with their individual outputs integrated to form a comprehensive classification result. This merging allows the system to leverage the strengths of each technique while compensating for their individual weaknesses regarding extraneous information
2Measurement precision
If multiple classification techniques are employed to improve extraction accuracy, then reliability improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent implements a dynamic classification system where the choice and application of different classification techniques can be adapted based on the characteristics of the input text. The system can dynamically select which techniques to apply to different portions of text or adjust the weighting of different classifiers based on their performance, thereby achieving high accuracy while managing complexity through adaptive behavior
Solution Approach 2:
The patent incorporates feedback mechanisms where the results from different classification techniques are evaluated and used to refine the overall classification process. The system learns from the performance of each technique and adjusts their application accordingly, using feedback loops to improve accuracy while avoiding unnecessary complexity by only applying techniques that demonstrate value
3Reliability
If manual inspection methods are used to enrich product databases with attribute-value pairs, then accuracy can be maintained, but productivity and efficiency are severely limited
Solution Approach 1:
The patent implements self-service classification where the system automatically processes and classifies text data without requiring manual inspection. The multiple classification techniques work together to autonomously extract attribute-value pairs from product descriptions and other text sources, enabling the system to serve itself in enriching databases at high speed while maintaining accuracy through the combined power of diverse classification approaches
Data Source
AI summary
A body of text comprises a plurality of unknown attributes and a plurality of unknown values. A first classification sub-component labels a first portion of the plurality of unknown values as a first set of values, whereas a second classification sub-component labels a portion of the plurality of unknown attributes as a set of attributes and a second portion of the plurality of unknown values as a second set of values. Learning models implemented by the first and second classification subcomponents are updated based on the set of attributes and the first and second set of values. The first classification sub-component implements at least one supervised classification technique, whereas the second classification sub-component implements an unsupervised and/or semi-supervised classification technique. Active learning may be employed to provide at least one of a corrected attribute and/or corrected value that may be used to update the learning models.