Iterative Semantic Model Generation from Textual Listings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information extraction techniques are inefficient for processing informal and idiosyncratic textual listings due to the need for human supervision and manual data generation, making it costly and time-consuming to develop semantic models for each new domain.
Innovation Solution
A processing device generates semantic models by identifying main concept words and attribute words through an iterative parsing and expansion process, clustering words, and using user input to refine the model, allowing for unsupervised or semi-supervised operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment and human supervision are used to generate semantic models, then the quality and accuracy of information extraction is improved, but the time and cost required to develop semantic models increases significantly
Solution Approach 1:
The system performs self-training by automatically generating training data from unannotated textual listings through iterative parsing and semantic model refinement. The semantic model autonomously identifies main concept words and attribute words, generates training examples, and improves itself without requiring manual annotation of training data, thereby eliminating the time-consuming human supervision step while maintaining extraction accuracy.
Solution Approach 2:
The system performs preliminary automated processing of textual listings to generate candidate training data before final model training. By pre-processing the unannotated text corpus to identify potential main concepts and attributes, the system prepares high-quality training materials in advance, reducing the subsequent manual work required and accelerating overall model development.
2Measurement precision
If manual assessment is used to create domain-specific semantic models, then the model quality is improved, but the complexity and cost of development increases
Solution Approach 1:
The system automatically adapts to new domains by performing self-training on domain-specific textual listings. When applied to a new domain (e.g., real estate, e-commerce), the system autonomously parses domain-specific listings, identifies domain-relevant main concepts and attributes, and generates domain-adapted training data without requiring manual domain expertise or complex domain-specific configuration, thereby reducing development complexity while maintaining model quality.
Solution Approach 2:
The semantic model framework is designed to be domain-agnostic and universally applicable. The same core parsing and training mechanisms work across different domains (real estate, e-commerce, services) by automatically adapting to domain-specific vocabulary and structures, eliminating the need for separate manual model development for each domain and reducing overall system complexity.
3Measurement precision
If manual annotation of training data is performed, then the semantic model achieves high accuracy, but the productivity and speed of model development decreases
Solution Approach 1:
The system generates its own training data automatically by parsing unannotated textual listings and using the semantic model to identify main concept words and attribute words. This self-generated training data eliminates the need for manual annotation while maintaining sufficient quality for accurate information extraction, thereby dramatically increasing model development speed without sacrificing extraction accuracy.
Solution Approach 2:
The system merges the data preprocessing, training data generation, and model training processes into an integrated automated pipeline. By combining these previously separate steps (manual data collection, manual annotation, model training) into a unified self-training process, the system achieves both high productivity and high accuracy simultaneously.
Data Source
AI summary
A corpus of textual listings is received and main concept words and attribute words therein are identified via an iterative process of parsing listings and expanding a semantic model. During the parsing phase, the corpus of textual listings is parsed to tag one or more head noun words and/or one or more identifier words in each listing based on previously identified main concept words or using a head noun identification rule. Once substantially each listing in the corpus has been parsed in this manner, the expansion phase assigns head noun words as main concept words and modifier words as attribute words, where possible. During the next iteration, the newly identified main concept words and/or attribute words are used to further parse the listings. These iterations are repeated until a termination condition is reached. Remaining words in the corpus are clustered based on the main concept words and attribute words.