Venue Classification via Context-Sensitive Attribute Extraction
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Solution Overview
Problem
Existing methods for analyzing venue data from websites are inadequate as they perform full text searches without considering synonyms or context, leading to inaccurate classification and lack of detail extraction.
Innovation Solution
A method and system that scans venue websites for data from HTML, PDF, and images, analyzes verifiable data by comparing it to preliminary data, determines attribute representation, and classifies venues based on attribute similarities and probabilities, allowing for context-sensitive extraction and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If full text searches are performed on venue websites, then information extraction is comprehensive, but accuracy and context understanding deteriorate
Solution Approach 1:
The system changes the parameter of text analysis from simple keyword matching to semantic understanding by implementing synonym recognition and contextual analysis. This allows the system to maintain comprehensive information extraction while improving classification accuracy through better understanding of text meaning.
Solution Approach 2:
The system introduces an intermediary layer of semantic analysis between raw text and classification results. This intermediary process includes synonym matching, contextual understanding, and attribute extraction, which bridges the gap between comprehensive data collection and accurate classification.
2Productivity
If broad classification categories are used, then classification speed is improved, but detail extraction and context understanding deteriorate
Solution Approach 1:
The system segments the classification process into multiple stages: initial broad categorization followed by detailed attribute extraction. This segmentation allows the system to quickly assign venues to broad categories while subsequently extracting detailed information and context without compromising overall processing efficiency.
Solution Approach 2:
The system adds another dimension to classification by implementing hierarchical categorization. Venues are first classified into broad categories, then further differentiated by specific attributes and characteristics. This multi-dimensional approach maintains speed through the first-level classification while recovering detailed information through additional attribute extraction.
3Measurement precision
If strict character matching is used for searches, then search precision is improved, but synonym recognition and context understanding deteriorate
Solution Approach 1:
The system implements multi-functionality in the search mechanism by combining strict character matching with synonym recognition and contextual analysis. The search function serves multiple purposes: exact matching for precision, synonym matching for versatility, and contextual understanding for accurate classification, thereby resolving the contradiction between precision and adaptability.
Data Source
AI summary
A method and system classifies a venue by analyzing venue data from a venue website. The method includes receiving preliminary venue-related data. The method includes scanning the venue website to retrieve venue data, wherein scanning the venue website includes retrieving the venue data from HTML pages, text documents, PDF documents, and images. The method includes retrieving verifiable venue data from the venue data. The verifiable venue data is a subset of the venue data. The method includes analyzing the verifiable venue data by comparing the verifiable venue data to the preliminary venue-related data and determining a probability level for the venue URL from the comparison. If the probability level for the venue URL is equal or greater than a first probability level, the venue website data is further analyzed to extract attributes and attribute counts in a robust and context-sensitive way. The method includes determining the percentage of the attribute representation from the total number of preselected attributes in the venue data and classifying the venue based on the percentage of the attribute representation.